bibliography.bib

@unpublished{schrodinger_llc_pymol_2015,
	title = {The {PyMOL} {Molecular} {Graphics} {System}, {Version} 1.8},
	author = {{Schrödinger, LLC}},
	month = nov,
	year = {2015},
}

@article{kim_pubchem_2021,
	title = {{PubChem} in 2021: new data content and improved web interfaces},
	volume = {49},
	issn = {1362-4962},
	shorttitle = {{PubChem} in 2021},
	doi = {10.1093/nar/gkaa971},
	abstract = {PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).},
	number = {D1},
	journal = {Nucleic Acids Research},
	author = {Kim, Sunghwan and Chen, Jie and Cheng, Tiejun and Gindulyte, Asta and He, Jia and He, Siqian and Li, Qingliang and Shoemaker, Benjamin A. and Thiessen, Paul A. and Yu, Bo and Zaslavsky, Leonid and Zhang, Jian and Bolton, Evan E.},
	month = jan,
	year = {2021},
	pmid = {33151290},
	pmcid = {PMC7778930},
	keywords = {Software, Databases, Humans, User-Computer Interface, Chemical, COVID-19, Drug Discovery, Epidemics, Information Storage and Retrieval, Internet, Public Health, SARS-CoV-2},
	pages = {D1388--D1395},
}

@article{trott_autodock_2009,
	title = {{AutoDock} {Vina}: {Improving} the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading},
	issn = {01928651, 1096987X},
	shorttitle = {{AutoDock} {Vina}},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/jcc.21334},
	doi = {10.1002/jcc.21334},
	abstract = {AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared to the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multi-core machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.},
	urldate = {2022-07-12},
	journal = {Journal of Computational Chemistry},
	author = {Trott, Oleg and Olson, Arthur J.},
	year = {2009},
	pages = {NA--NA},
	file = {Trott e Olson - 2009 - AutoDock Vina Improving the speed and accuracy of.pdf:C\:\\Users\\Utente\\Zotero\\storage\\CIZATY5K\\Trott e Olson - 2009 - AutoDock Vina Improving the speed and accuracy of.pdf:application/pdf},
}

@article{oboyle_open_2011,
	title = {Open {Babel}: {An} open chemical toolbox},
	volume = {3},
	issn = {1758-2946},
	url = {https://doi.org/10.1186/1758-2946-3-33},
	doi = {10.1186/1758-2946-3-33},
	abstract = {A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats.},
	number = {1},
	journal = {Journal of Cheminformatics},
	author = {O'Boyle, Noel M. and Banck, Michael and James, Craig A. and Morley, Chris and Vandermeersch, Tim and Hutchison, Geoffrey R.},
	month = oct,
	year = {2011},
	pages = {33},
	file = {O'Boyle et al. - 2011 - Open Babel An open chemical toolbox.pdf:C\:\\Users\\Utente\\Zotero\\storage\\ANQPSDLV\\O'Boyle et al. - 2011 - Open Babel An open chemical toolbox.pdf:application/pdf},
}

@article{morris_autodock4_2009,
	title = {{AutoDock4} and {AutoDockTools4}: {Automated} docking with selective receptor flexibility},
	volume = {30},
	issn = {1096-987X},
	shorttitle = {{AutoDock4} and {AutoDockTools4}},
	doi = {10.1002/jcc.21256},
	abstract = {We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.},
	number = {16},
	journal = {Journal of Computational Chemistry},
	author = {Morris, Garrett M. and Huey, Ruth and Lindstrom, William and Sanner, Michel F. and Belew, Richard K. and Goodsell, David S. and Olson, Arthur J.},
	month = dec,
	year = {2009},
	pmid = {19399780},
	pmcid = {PMC2760638},
	keywords = {Models, Molecular, Software, Ligands, Proteins, Protein Binding},
	pages = {2785--2791},
}

@article{burley_rcsb_2021,
	title = {{RCSB} {Protein} {Data} {Bank}: powerful new tools for exploring {3D} structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences},
	volume = {49},
	issn = {1362-4962},
	shorttitle = {{RCSB} {Protein} {Data} {Bank}},
	doi = {10.1093/nar/gkaa1038},
	abstract = {The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including {\textbackslash}textgreater660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with {\textbackslash}textgreater40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.},
	number = {D1},
	journal = {Nucleic Acids Research},
	author = {Burley, Stephen K. and Bhikadiya, Charmi and Bi, Chunxiao and Bittrich, Sebastian and Chen, Li and Crichlow, Gregg V. and Christie, Cole H. and Dalenberg, Kenneth and Di Costanzo, Luigi and Duarte, Jose M. and Dutta, Shuchismita and Feng, Zukang and Ganesan, Sai and Goodsell, David S. and Ghosh, Sutapa and Green, Rachel Kramer and Guranović, Vladimir and Guzenko, Dmytro and Hudson, Brian P. and Lawson, Catherine L. and Liang, Yuhe and Lowe, Robert and Namkoong, Harry and Peisach, Ezra and Persikova, Irina and Randle, Chris and Rose, Alexander and Rose, Yana and Sali, Andrej and Segura, Joan and Sekharan, Monica and Shao, Chenghua and Tao, Yi-Ping and Voigt, Maria and Westbrook, John D. and Young, Jasmine Y. and Zardecki, Christine and Zhuravleva, Marina},
	month = jan,
	year = {2021},
	pmid = {33211854},
	pmcid = {PMC7779003},
	keywords = {Software, Databases, Humans, Protein, Proteins, COVID-19, SARS-CoV-2, Bioengineering, Biomedical Research, Biotechnology, Computational Biology, Macromolecular Substances, Pandemics, Protein Conformation, Viral Proteins},
	pages = {D437--D451},
	file = {Burley et al. - 2021 - RCSB Protein Data Bank powerful new tools for exp.pdf:C\:\\Users\\Utente\\Zotero\\storage\\EZCMDDJG\\Burley et al. - 2021 - RCSB Protein Data Bank powerful new tools for exp.pdf:application/pdf},
}

@article{berman_protein_2000,
	title = {The {Protein} {Data} {Bank}},
	volume = {28},
	issn = {0305-1048},
	doi = {10.1093/nar/28.1.235},
	abstract = {The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.},
	number = {1},
	journal = {Nucleic Acids Research},
	author = {Berman, H. M. and Westbrook, J. and Feng, Z. and Gilliland, G. and Bhat, T. N. and Weissig, H. and Shindyalov, I. N. and Bourne, P. E.},
	month = jan,
	year = {2000},
	pmid = {10592235},
	pmcid = {PMC102472},
	keywords = {Databases, Proteins, Information Storage and Retrieval, Internet, Protein Conformation, Factual, Magnetic Resonance Spectroscopy},
	pages = {235--242},
}

@article{chmiel_deleterious_2019,
	title = {Deleterious {Effects} of {Neonicotinoid} {Pesticides} on {Drosophila} melanogaster {Immune} {Pathways}},
	volume = {10},
	url = {https://journals.asm.org/doi/10.1128/mBio.01395-19},
	doi = {10.1128/mBio.01395-19},
	number = {5},
	urldate = {2022-09-14},
	journal = {mBio},
	author = {Chmiel, John A. and Daisley, Brendan A. and Burton, Jeremy P. and Reid, Gregor},
	month = oct,
	year = {2019},
	note = {Publisher: American Society for Microbiology},
	pages = {e01395--19},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\NPE44PYM\\Chmiel et al. - 2019 - Deleterious Effects of Neonicotinoid Pesticides on.pdf:application/pdf},
}

@article{chmiel_understanding_2020,
	title = {Understanding the {Effects} of {Sublethal} {Pesticide} {Exposure} on {Honey} {Bees}: {A} {Role} for {Probiotics} as {Mediators} of {Environmental} {Stress}},
	volume = {8},
	issn = {2296-701X},
	shorttitle = {Understanding the {Effects} of {Sublethal} {Pesticide} {Exposure} on {Honey} {Bees}},
	url = {https://www.frontiersin.org/articles/10.3389/fevo.2020.00022},
	abstract = {Managed populations of the European honey bee (Apis mellifera) support the production of a global food supply. This important role in modern agriculture has rendered honey bees vulnerable to the noxious effects of anthropogenic stressors such as pesticides. Although the deleterious outcomes of lethal pesticide exposure on honey bee health and performance are apparent, the ominous role of sublethal pesticide exposure is an emerging concern as well. Here, we use a data harvesting approach to better understand the toxicological effects of pesticide exposure across the honey bee life cycle. Through compiling adult- and larval-specific median lethal dose (LD50) values from 93 published data sources, LD50 estimates for insecticides, herbicides, acaricides, and fungicides are highly variable across studies, especially for herbicides and fungicides, which are underrepresented in the meta-data set. Alongside major discrepancies in these reported values, further examination of the compiled data suggested that LD50 may not be an ideal metric for honey bee risk assessment. We also discuss how sublethal effects of pesticide exposure, which are not typically measured in LD50 studies, can diminish honey bee reproduction, immunity, cognition, and overall physiological functioning, leading to suboptimal honey bee performance and population reduction. In consideration of actionable solutions to mitigate the effects of sublethal pesticide exposure, we have identified the potential for probiotic supplementation as a promising strategy that can be easily incorporated alongside current agricultural infrastructure and apicultural management practices. Probiotic supplementation is regularly employed in apiculture but the potential for evidence-based targeted approaches has not yet been fully explored within a formal toxicological context. We discuss the benefits, practical considerations, and limitations for the use and delivery of probiotics to hives. Ultimately, by subverting the sublethal effects of pesticides we can help improve the long-term survival of these critical pollinators.},
	urldate = {2022-09-14},
	journal = {Frontiers in Ecology and Evolution},
	author = {Chmiel, John A. and Daisley, Brendan A. and Pitek, Andrew P. and Thompson, Graham J. and Reid, Gregor},
	year = {2020},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\6BZSSDYN\\Chmiel et al. - 2020 - Understanding the Effects of Sublethal Pesticide E.pdf:application/pdf},
}

@article{di_noi_review_2021,
	title = {Review on {Sublethal} {Effects} of {Environmental} {Contaminants} in {Honey} {Bees} ({Apis} mellifera), {Knowledge} {Gaps} and {Future} {Perspectives}},
	volume = {18},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1660-4601},
	url = {https://www.mdpi.com/1660-4601/18/4/1863},
	doi = {10.3390/ijerph18041863},
	abstract = {Honey bees and the pollination services they provide are fundamental for agriculture and biodiversity. Agrochemical products and other classes of contaminants, such as trace elements and polycyclic aromatic hydrocarbons, contribute to the general decline of bees’ populations. For this reason, effects, and particularly sublethal effects of contaminants need to be investigated. We conducted a review of the existing literature regarding the type of effects evaluated in Apis mellifera, collecting information about regions, methodological approaches, the type of contaminants, and honey bees’ life stages. Europe and North America are the regions in which A. mellifera biological responses were mostly studied and the most investigated compounds are insecticides. A. mellifera was studied more in the laboratory than in field conditions. Through the observation of the different responses examined, we found that there were several knowledge gaps that should be addressed, particularly within enzymatic and molecular responses, such as those regarding the immune system and genotoxicity. The importance of developing an integrated approach that combines responses at different levels, from molecular to organism and population, needs to be highlighted in order to evaluate the impact of anthropogenic contamination on this pollinator species.},
	language = {en},
	number = {4},
	urldate = {2022-09-14},
	journal = {International Journal of Environmental Research and Public Health},
	author = {Di Noi, Agata and Casini, Silvia and Campani, Tommaso and Cai, Giampiero and Caliani, Ilaria},
	month = jan,
	year = {2021},
	note = {Number: 4
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {bees decline, honey bees, methodological approach, monitoring strategies, plant protection products, sublethal effects},
	pages = {1863},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\7J6MNFLD\\Di Noi et al. - 2021 - Review on Sublethal Effects of Environmental Conta.pdf:application/pdf;Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\ZNTWULFK\\1863.html:text/html},
}

@article{di_prisco_neonicotinoid_2013,
	title = {Neonicotinoid clothianidin adversely affects insect immunity and promotes replication of a viral pathogen in honey bees},
	volume = {110},
	url = {https://www.pnas.org/doi/full/10.1073/pnas.1314923110},
	doi = {10.1073/pnas.1314923110},
	number = {46},
	urldate = {2022-09-14},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Di Prisco, Gennaro and Cavaliere, Valeria and Annoscia, Desiderato and Varricchio, Paola and Caprio, Emilio and Nazzi, Francesco and Gargiulo, Giuseppe and Pennacchio, Francesco},
	month = nov,
	year = {2013},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {18466--18471},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\3MNCI4J3\\Di Prisco et al. - 2013 - Neonicotinoid clothianidin adversely affects insec.pdf:application/pdf},
}

@article{forli_computational_2016,
	title = {Computational protein–ligand docking and virtual drug screening with the {AutoDock} suite},
	volume = {11},
	copyright = {2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1750-2799},
	url = {https://www.nature.com/articles/nprot.2016.051},
	doi = {10.1038/nprot.2016.051},
	abstract = {This protocol describes the use of the AutoDock suite for computational docking in the study of protein–ligand interactions. A number of methods are described ranging from basic docking of drug molecules to virtual screening using a large ligand library of chemical compounds.},
	language = {en},
	number = {5},
	urldate = {2022-09-14},
	journal = {Nature Protocols},
	author = {Forli, Stefano and Huey, Ruth and Pique, Michael E. and Sanner, Michel F. and Goodsell, David S. and Olson, Arthur J.},
	month = may,
	year = {2016},
	note = {Number: 5
Publisher: Nature Publishing Group},
	keywords = {Software, Computational biophysics, Structure-based drug design, Virtual drug screening},
	pages = {905--919},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\ZIJXYWEH\\nprot.2016.html:text/html;Versione accettata:C\:\\Users\\Utente\\Zotero\\storage\\QAGGSV3Y\\Forli et al. - 2016 - Computational protein–ligand docking and virtual d.pdf:application/pdf},
}

@article{gallai_economic_2009,
	title = {Economic valuation of the vulnerability of world agriculture confronted with pollinator decline},
	volume = {68},
	issn = {0921-8009},
	url = {https://www.sciencedirect.com/science/article/pii/S0921800908002942},
	doi = {10.1016/j.ecolecon.2008.06.014},
	abstract = {There is mounting evidence of pollinator decline all over the world and consequences in many agricultural areas could be significant. We assessed these consequences by measuring 1) the contribution of insect pollination to the world agricultural output economic value, and 2) the vulnerability of world agriculture in the face of pollinator decline. We used a bioeconomic approach, which integrated the production dependence ratio on pollinators, for the 100 crops used directly for human food worldwide as listed by FAO. The total economic value of pollination worldwide amounted to €153 billion, which represented 9.5\% of the value of the world agricultural production used for human food in 2005. In terms of welfare, the consumer surplus loss was estimated between €190 and €310 billion based upon average price elasticities of − 1.5 to − 0.8, respectively. Vegetables and fruits were the leading crop categories in value of insect pollination with about €50 billion each, followed by edible oil crops, stimulants, nuts and spices. The production value of a ton of the crop categories that do not depend on insect pollination averaged €151 while that of those that are pollinator-dependent averaged €761. The vulnerability ratio was calculated for each crop category at the regional and world scales as the ratio between the economic value of pollination and the current total crop value. This ratio varied considerably among crop categories and there was a positive correlation between the rate of vulnerability to pollinators decline of a crop category and its value per production unit. Looking at the capacity to nourish the world population after pollinator loss, the production of 3 crop categories – namely fruits, vegetables, and stimulants - will clearly be below the current consumption level at the world scale and even more so for certain regions like Europe. Yet, although our valuation clearly demonstrates the economic importance of insect pollinators, it cannot be considered as a scenario since it does not take into account the strategic responses of the markets.},
	language = {en},
	number = {3},
	urldate = {2022-09-14},
	journal = {Ecological Economics},
	author = {Gallai, Nicola and Salles, Jean-Michel and Settele, Josef and Vaissière, Bernard E.},
	month = jan,
	year = {2009},
	keywords = {Agriculture, Crop, Ecosystem service, Pollination, Valuation, Vulnerability},
	pages = {810--821},
	file = {j.ecolecon.2008.06.014.pdf:C\:\\Users\\Utente\\Zotero\\storage\\2MJ3I6VF\\j.ecolecon.2008.06.014.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\LKQ9Y8N8\\S0921800908002942.html:text/html;Versione inviata:C\:\\Users\\Utente\\Zotero\\storage\\NXM38TEX\\Gallai et al. - 2009 - Economic valuation of the vulnerability of world a.pdf:application/pdf},
}

@article{li_new_2007,
	title = {New {Mechanism} of {Organophosphorus} {Pesticide}-induced {Immunotoxicity}},
	volume = {74},
	doi = {10.1272/jnms.74.92},
	abstract = {Organophosphorus pesticides (OPs) are widely used throughout the world as insecticides in agriculture and as eradicating agents for termites around homes. The main toxicity of OPs is neurotoxicity, which is caused by the inhibition of acetylcholinesterase. OPs also affect the immune response, including effects on antibody production, interleukin-2 production, T cell proliferation, decrease of CD5 cells, and increases of CD26 cells and autoantibodies, Th1/Th2 cytokine profiles, and the inhibition of natural killer (NK) cell, lymphokine-activated killer (LAK) cell, and cytotoxic T lymphocyte (CTL) activities. However, there have been few studies of the mechanism of OP-induced immunotoxicity, especially the mechanism of OP-induced inhibition of cytolytic activity of killer cells. This study reviews new mechanisms of OP-induced inhibition of the activities of NK cells, LAK cells, and CTLs. It has been reported that NK cells, LAK cells, and CTLs induce cell death in tumors or virus-infected target cells by two main mechanisms. The first mechanism is direct release of cytolytic granules that contain the pore-forming protein perforin, several serine proteases termed granzymes, and granulysin by exocytosis to kill target cells, which is called the granule exocytosis pathway. The second mechanism is mediated by the Fas ligand (Fas-L)/Fas pathway, in which FasL (CD95 L), a surface membrane ligand of the killer cell cross links with the target cell's surface death receptor Fas (CD95) to induce apoptosis of the target cells. To date, it has been reported that OPs inhibit NK cell, LAK cell, and CTL activities by at least the following three mechanisms: 1) OPs impair the granule exocytosis pathway of NK cells, LAK cells, and CTLs by inhibiting the activity of granzymes, and by decreasing the intracellular levels of perforin, granzyme A, and granulysin, which were mediated by inducing degranulation of NK cells and by inhibiting the transcription of the mRNAs of perforin, granzyme A, and granulysin. 2) OPs impair the FasL/Fas pathway of NK cells, LAK cells, and CTLs, as investigated by using perforin-knockout mice, in which the granule exocytosis pathway of NK cells does not function and only the FasL/Fas pathway remains functional. 3) OPs induce apoptosis of immune cells.},
	number = {2},
	journal = {Journal of Nippon Medical School},
	author = {Li, Qing},
	year = {2007},
	keywords = {apoptosis, granulysin, granzyme, natural killer cells, organophosphorus pesticide, perforin},
	pages = {92--105},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\2MVV6EFF\\Li - 2007 - New Mechanism of Organophosphorus Pesticide-induce.pdf:application/pdf;J-Stage - Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\DJG52D5Y\\_article.html:text/html},
}

@article{naqvi_advancements_nodate,
	title = {Advancements in {Docking} and {Molecular} {Dynamics} {Simulations} {Towards} {Ligand}-receptor {Interactions} and {Structure}-function {Relationships}},
	volume = {18},
	url = {https://www.eurekaselect.com/article/93975},
	abstract = {Protein-ligand interaction is an imperative subject in structure-based drug design and protein
function prediction process. Molecular docking is a computational method which predicts the binding
of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity
of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations
are widely used in combination to predict the binding modes, binding affinities and stability of
different protein-ligand systems. With advancements in algorithms and computational power, molecular
dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic
level. These methods in association with experimental support have been of great value in modern drug
discovery and development. Nowadays, it has become an increasingly significant method in drug discovery
process. In this review, we focus on protein-ligand interactions using molecular docking, virtual
screening and molecular dynamics simulations. Here, we cover an overview of the available methods
for molecular docking and molecular dynamics simulations, and their advancement and applications in
the area of modern drug discovery. The available docking software and their advancement including
application examples of different approaches for drug discovery are also discussed. We have also introduced
the physicochemical foundations of molecular docking and simulations, mainly from the perception
of bio-molecular interactions.},
	language = {en},
	number = {20},
	urldate = {2022-09-14},
	journal = {Current Topics in Medicinal Chemistry},
	author = {Naqvi, Ahmad Abu Turab and Mohammad, Taj and Hasan, Gulam Mustafa and Hassan, Md Imtaiyaz},
	pages = {1755--1768},
	file = {Naqvi et al. - Advancements in Docking and Molecular Dynamics Sim.pdf:C\:\\Users\\Utente\\Zotero\\storage\\U8ICFHE4\\Naqvi et al. - Advancements in Docking and Molecular Dynamics Sim.pdf:application/pdf;Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\XT6MXLU3\\93975.html:text/html},
}

@article{sanchez-bayo_pesticide_2014,
	title = {Pesticide {Residues} and {Bees} – {A} {Risk} {Assessment}},
	volume = {9},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0094482},
	doi = {10.1371/journal.pone.0094482},
	abstract = {Bees are essential pollinators of many plants in natural ecosystems and agricultural crops alike. In recent years the decline and disappearance of bee species in the wild and the collapse of honey bee colonies have concerned ecologists and apiculturalists, who search for causes and solutions to this problem. Whilst biological factors such as viral diseases, mite and parasite infections are undoubtedly involved, it is also evident that pesticides applied to agricultural crops have a negative impact on bees. Most risk assessments have focused on direct acute exposure of bees to agrochemicals from spray drift. However, the large number of pesticide residues found in pollen and honey demand a thorough evaluation of all residual compounds so as to identify those of highest risk to bees. Using data from recent residue surveys and toxicity of pesticides to honey and bumble bees, a comprehensive evaluation of risks under current exposure conditions is presented here. Standard risk assessments are complemented with new approaches that take into account time-cumulative effects over time, especially with dietary exposures. Whilst overall risks appear to be low, our analysis indicates that residues of pyrethroid and neonicotinoid insecticides pose the highest risk by contact exposure of bees with contaminated pollen. However, the synergism of ergosterol inhibiting fungicides with those two classes of insecticides results in much higher risks in spite of the low prevalence of their combined residues. Risks by ingestion of contaminated pollen and honey are of some concern for systemic insecticides, particularly imidacloprid and thiamethoxam, chlorpyrifos and the mixtures of cyhalothrin and ergosterol inhibiting fungicides. More attention should be paid to specific residue mixtures that may result in synergistic toxicity to bees.},
	language = {en},
	number = {4},
	urldate = {2022-09-14},
	journal = {PLOS ONE},
	author = {Sanchez-Bayo, Francisco and Goka, Koichi},
	month = apr,
	year = {2014},
	note = {Publisher: Public Library of Science},
	keywords = {Bees, Honey, Honey bees, Insecticides, Larvae, Pesticides, Pollen, Toxicity},
	pages = {e94482},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\GTWHSVIS\\Sanchez-Bayo e Goka - 2014 - Pesticide Residues and Bees – A Risk Assessment.pdf:application/pdf;Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\8SEFDP7I\\article.html:text/html},
}

@article{sgolastra_bees_2020,
	title = {Bees and pesticide regulation: {Lessons} from the neonicotinoid experience},
	volume = {241},
	issn = {0006-3207},
	shorttitle = {Bees and pesticide regulation},
	url = {https://www.sciencedirect.com/science/article/pii/S0006320719310912},
	doi = {10.1016/j.biocon.2019.108356},
	abstract = {Neonicotinoid insecticides have been signaled as an important driver of widespread declines in bee diversity and abundance. Neonicotinoids were registered in the 1990s and by 2010 accounted for one third of the global insecticide market. Following a moratorium in 2013, their use on open-field crops was completely banned in the EU in 2018. Pesticide regulation should be based on solid and updated scientific evidence, whereby products showing unacceptable effects on the environment are not approved. Clearly, pesticide regulation failed to detect the ecological threats posed by neonicotinoids. We argue that at the time neonicotinoids were authorized, risk assessment (RA) protocols were inadequate to detect some of the risks associated with neonicotinoid properties, including high efficacy, long persistence, high systemicity, high mobility, and application versatility. We advocate for the adoption of a more holistic RA approach that should account for: a) temporal and spatial dimensions of pesticide exposure; b) co-exposure to multiple compounds; c) differences among bee species with different life histories in levels of exposure and sensitivity; and d) sublethal effects (mostly ignored in current RA procedures). We also argue that regulatory studies conducted to support pesticide registration should be publicly available, and that pesticide regulation should not be discontinued once a product has been authorized. We should use the knowledge acquired through the neonicotinoid experience as an opportunity to profoundly revise bee RA schemes. These efforts should be initiated promptly; the neonicotinoid story has also taught us that the regulatory system is reluctant to react.},
	language = {en},
	urldate = {2022-09-14},
	journal = {Biological Conservation},
	author = {Sgolastra, Fabio and Medrzycki, Piotr and Bortolotti, Laura and Maini, Stefano and Porrini, Claudio and Simon-Delso, Noa and Bosch, Jordi},
	month = jan,
	year = {2020},
	keywords = {Agriculture, Bee decline, Environmental risk assessment, Plant protection products, Pollinators},
	pages = {108356},
	file = {ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\PY93HQCX\\S0006320719310912.html:text/html},
}

@article{meng_molecular_2011,
	title = {Molecular {Docking}: {A} powerful approach for structure-based drug discovery},
	volume = {7},
	issn = {1573-4099},
	shorttitle = {Molecular {Docking}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3151162/},
	abstract = {Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.},
	number = {2},
	urldate = {2022-09-14},
	journal = {Current computer-aided drug design},
	author = {Meng, Xuan-Yu and Zhang, Hong-Xing and Mezei, Mihaly and Cui, Meng},
	month = jun,
	year = {2011},
	pmid = {21534921},
	pmcid = {PMC3151162},
	pages = {146--157},
	file = {PubMed Central Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\9LPZZ2VM\\Meng et al. - 2011 - Molecular Docking A powerful approach for structu.pdf:application/pdf},
}

@incollection{roy_chapter_2015,
	address = {Boston},
	title = {Chapter 10 - {Other} {Related} {Techniques}},
	isbn = {978-0-12-801505-6},
	url = {https://www.sciencedirect.com/science/article/pii/B9780128015056000107},
	abstract = {With the advances in computational resources, there is an increasing urge among the computational researchers to make the in silico approaches fast, convenient, reproducible, acceptable, and sensible ones. Along with the typical two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods, approaches like pharmacophore, structure-based docking studies, and combinations of ligand- and structure-based approaches like comparative residue interaction analysis (CoRIA) and comparative binding energy analysis (COMBINE) have gained a significant popularity in the computational drug design process. A pharmacophore can be developed either in a ligand-based method, by superposing a set of active molecules and extracting common chemical features which are vital for their bioactivity; or in a structure-based manner, by probing probable interaction points between the macromolecular target and ligands. The interaction of protein and ligand molecules with each other is one of the interesting studies in modern molecular biology and molecular recognition. This interaction can well be explained with the conceptof a docking study to show how a molecule can bind to another molecule to exert the bioactivity. Docking and pharmacophore are non-QSAR approaches in in silico drug design that can support the QSAR findings. Approaches like CoRIA and COMBINE can use information generated from the ligand–receptor complexes to extract the critical clue concerning the types of significant interaction at the level of both the receptor and the ligand. Employing the abovementioned ligand- and structure-based methodologies and chemical libraries, virtual screening (VS) emerged as an important tool in the quest to develop novel drug compounds. VS serves as an efficient computational tool that integrates structural data with lead optimization as a cost-effective approach to drug discovery.},
	language = {en},
	urldate = {2022-09-14},
	booktitle = {Understanding the {Basics} of {QSAR} for {Applications} in {Pharmaceutical} {Sciences} and {Risk} {Assessment}},
	publisher = {Academic Press},
	author = {Roy, Kunal and Kar, Supratik and Das, Rudra Narayan},
	editor = {Roy, Kunal and Kar, Supratik and Das, Rudra Narayan},
	month = jan,
	year = {2015},
	doi = {10.1016/B978-0-12-801505-6.00010-7},
	keywords = {COMBINE, CoRIA, database mining, docking, pharmacophore, virtual screening},
	pages = {357--425},
	file = {ScienceDirect Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\FJRVKVG4\\Roy et al. - 2015 - Chapter 10 - Other Related Techniques.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\D6SVCITE\\B9780128015056000107.html:text/html},
}

@incollection{roy_chapter_2015-1,
	address = {Boston},
	title = {Chapter 5 - {Computational} {Chemistry}},
	isbn = {978-0-12-801505-6},
	url = {https://www.sciencedirect.com/science/article/pii/B9780128015056000053},
	abstract = {The expansion of theoretical chemistry and molecular modeling analysis has been assisted by advanced computational platforms. Computers not only can be used to solve complex chemical equations, but also to provide a suitable graphical viewer package to hypothetically visualize simulated operations and their output. Hence, computer technology proves to be an indispensable tool for carrying out molecular modeling operations. In fact, one can easily run such a job using the default values specified by the provider of a specific program. In order to interpret the outcome of a molecular modeling operation, one needs to have sufficient knowledge of the theoretical bases of the operations involved. In this chapter, we have attempted to provide some basics of different aspects of computational chemistry, where computer applications are crucial. In addition, we have provided the basic theoretical foundation involved in such processes, on the assumption that readers are novices in this field. With a clear conception of the theoretical bases, one can not only interpret the results of such modeling operations, but also modify the process variables in accordance with the needs in question. While certain processes might give appreciable results for certain groups of chemicals under defined criteria, it may be the case that other processes may fail or not even be applicable. Hence, readers are advised to achieve a basic theoretical insight of the molecular modeling operations in the light of computational algorithms instead of considering them a “black-box tool” for obtaining data.},
	language = {en},
	urldate = {2022-09-14},
	booktitle = {Understanding the {Basics} of {QSAR} for {Applications} in {Pharmaceutical} {Sciences} and {Risk} {Assessment}},
	publisher = {Academic Press},
	author = {Roy, Kunal and Kar, Supratik and Das, Rudra Narayan},
	editor = {Roy, Kunal and Kar, Supratik and Das, Rudra Narayan},
	month = jan,
	year = {2015},
	doi = {10.1016/B978-0-12-801505-6.00005-3},
	keywords = {Computer, molecular dynamics (MD), molecular mechanics (MM), semiempirical quantum mechanics, simulation},
	pages = {151--189},
	file = {ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\C8WTVXX8\\B9780128015056000053.html:text/html},
}

@misc{noauthor_chapter_nodate,
	title = {Chapter 27 - {Practical} {Considerations} in {Virtual} {Screening} and {Molecular} {Docking} {\textbar} {Elsevier} {Enhanced} {Reader}},
	url = {https://reader.elsevier.com/reader/sd/pii/B9780128025086000272?token=1DC1AE6F74BAC4C0B2B62464E128A96D6659BA01F33FAE022EE248ED363685B3C988E8662C6E843338BA050A0E98062C&originRegion=eu-west-1&originCreation=20220914090420},
	language = {en},
	urldate = {2022-09-14},
	doi = {10.1016/B978-0-12-802508-6.00027-2},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\QNN73733\\Chapter 27 - Practical Considerations in Virtual S.pdf:application/pdf;Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\KW78X27B\\B9780128025086000272.html:text/html},
}

@book{menchaca_past_2020,
	title = {Past, {Present}, and {Future} of {Molecular} {Docking}},
	isbn = {978-1-78923-976-8},
	url = {https://www.intechopen.com/chapters/undefined/state.item.id},
	abstract = {The interface of any given ligand and protein—normally considered a macromolecule—of a known or predicted/modeled structure can be computed by determining each potential ligand position, resulting in an array of possibilities which are finally expressed in numerical energy values based on their thermodynamic affinity. Over the past few decades, this premier approach technique has proved to be crucial as an automated method in drug design and discovery, as well as in other fields. Data are retrieved from contour surface calculations for each ligand probe and can be analyzed to delineate regions of attraction on the basis of energy levels. Negative energy levels from contours are used to infer protein-ligand affinity clefts and are therefore relevant to drug design. Accordingly, molecular docking, framed as the “new microscope,” is part of a group of in silico computational techniques that enable the behavior of molecular chemistry to be analyzed and predicted in an inexpensive manner. From the starting point of framing the key terms in the binomial macromolecule-ligand docking approach, this chapter presents an introductory description of the progress made in this field of research over the past several years, in addition to present and future perspectives. This chapter presents a broad plethora of possibilities arising from the old docking alternatives to the current software technology and critically dissects and discusses the emerging trends. Despite the emergence of more degrees of freedom, a number of flexible conglomerates have not been well developed, and there are still computational limitations to solve, including several features in the focused technique. The present goals, such as molecular flexibility, binding entropy, and the presence of ions and solute conditions, are revisited with the purpose of anticipating the challenges, goals, and achievements in this field over the next few years or decades.},
	language = {en},
	urldate = {2022-09-14},
	publisher = {IntechOpen},
	author = {Menchaca, Thuluz Meza and Juárez-Portilla, Claudia and Zepeda, Rossana C.},
	month = feb,
	year = {2020},
	doi = {10.5772/intechopen.90921},
	note = {Publication Title: Drug Discovery and Development - New Advances},
	file = {Full text:C\:\\Users\\Utente\\Zotero\\storage\\IDSGFXTE\\Menchaca et al. - 2020 - Past, Present, and Future of Molecular Docking.pdf:application/pdf;Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\4ESBNHFY\\70982.html:text/html},
}

@article{mcnutt_gnina_2021,
	title = {{GNINA} 1.0: molecular docking with deep learning},
	volume = {13},
	issn = {1758-2946},
	shorttitle = {{GNINA} 1.0},
	doi = {10.1186/s13321-021-00522-2},
	abstract = {Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. GNINA, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58\% to 73\% and from 27\% to 37\%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31\% to 38\% and from 12\% to 16\%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of GNINA under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina .},
	language = {eng},
	number = {1},
	journal = {Journal of Cheminformatics},
	author = {McNutt, Andrew T. and Francoeur, Paul and Aggarwal, Rishal and Masuda, Tomohide and Meli, Rocco and Ragoza, Matthew and Sunseri, Jocelyn and Koes, David Ryan},
	month = jun,
	year = {2021},
	pmid = {34108002},
	pmcid = {PMC8191141},
	keywords = {Structure-based drug design, Molecular docking, Deep learning},
	pages = {43},
	file = {Full text:C\:\\Users\\Utente\\Zotero\\storage\\GYCQXFN8\\McNutt et al. - 2021 - GNINA 1.0 molecular docking with deep learning.pdf:application/pdf},
}

@misc{noauthor_molecular_nodate,
	title = {Molecular {Interactions} ({Noncovalent} {Interactions})},
	url = {https://williams.chemistry.gatech.edu/structure/molecular_interactions/mol_int.html},
	urldate = {2022-09-14},
	file = {Molecular Interactions (Noncovalent Interactions):C\:\\Users\\Utente\\Zotero\\storage\\IAZAZ4AP\\mol_int.html:text/html},
}

@article{bissantz_medicinal_2010,
	title = {A {Medicinal} {Chemist}’s {Guide} to {Molecular} {Interactions}},
	volume = {53},
	issn = {0022-2623},
	url = {https://doi.org/10.1021/jm100112j},
	doi = {10.1021/jm100112j},
	number = {14},
	urldate = {2022-09-14},
	journal = {Journal of Medicinal Chemistry},
	author = {Bissantz, Caterina and Kuhn, Bernd and Stahl, Martin},
	month = jul,
	year = {2010},
	note = {Publisher: American Chemical Society},
	pages = {5061--5084},
	file = {ACS Full Text Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\JGLXMPK5\\jm100112j.html:text/html;Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\NGSQ2ER9\\Bissantz et al. - 2010 - A Medicinal Chemist’s Guide to Molecular Interacti.pdf:application/pdf},
}

@article{phatak_high-throughput_2009,
	title = {High-throughput and in silico screenings in drug discovery},
	volume = {4},
	issn = {1746-0441},
	url = {https://doi.org/10.1517/17460440903190961},
	doi = {10.1517/17460440903190961},
	abstract = {Background: In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones. Objective: Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process. Conclusion: Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.},
	number = {9},
	urldate = {2022-09-14},
	journal = {Expert Opinion on Drug Discovery},
	author = {Phatak, Sharangdhar S and Stephan, Clifford C and Cavasotto, Claudio N},
	month = sep,
	year = {2009},
	pmid = {23480542},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1517/17460440903190961},
	keywords = {docking, drug discovery, high-throughput screening, in silico screening},
	pages = {947--959},
}

@article{jimenez-luna_artificial_2021,
	title = {Artificial intelligence in drug discovery: recent advances and future perspectives},
	volume = {16},
	issn = {1746-0441},
	shorttitle = {Artificial intelligence in drug discovery},
	url = {https://doi.org/10.1080/17460441.2021.1909567},
	doi = {10.1080/17460441.2021.1909567},
	abstract = {Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.},
	number = {9},
	urldate = {2022-09-14},
	journal = {Expert Opinion on Drug Discovery},
	author = {Jiménez-Luna, José and Grisoni, Francesca and Weskamp, Nils and Schneider, Gisbert},
	month = sep,
	year = {2021},
	pmid = {33779453},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/17460441.2021.1909567},
	keywords = {artificial intelligence, de novo drug design, Drug discovery, QSAR, synthesis prediction},
	pages = {949--959},
	file = {Full text:C\:\\Users\\Utente\\Zotero\\storage\\ENEVXZAQ\\Jiménez-Luna et al. - 2021 - Artificial intelligence in drug discovery recent .pdf:application/pdf},
}

@incollection{stanzione_chapter_2021,
	title = {Chapter {Four} - {Use} of molecular docking computational tools in drug discovery},
	volume = {60},
	url = {https://www.sciencedirect.com/science/article/pii/S0079646821000047},
	abstract = {Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.},
	language = {en},
	urldate = {2022-09-14},
	booktitle = {Progress in {Medicinal} {Chemistry}},
	publisher = {Elsevier},
	author = {Stanzione, Francesca and Giangreco, Ilenia and Cole, Jason C.},
	editor = {Witty, David R. and Cox, Brian},
	month = jan,
	year = {2021},
	doi = {10.1016/bs.pmch.2021.01.004},
	keywords = {Drug discovery, Homology modelling, Molecular interaction, Molecular target, Protein databases, Scoring functions, Small molecule databases, Structure-based drug design (SBDD), Virtual screening (VS)},
	pages = {273--343},
	file = {ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\XSWQC54N\\S0079646821000047.html:text/html},
}

@article{li_neonicotinoid_2015,
	title = {Neonicotinoid insecticide interact with honeybee odorant-binding protein: {Implication} for olfactory dysfunction},
	volume = {81},
	issn = {01418130},
	shorttitle = {Neonicotinoid insecticide interact with honeybee odorant-binding protein},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0141813015006029},
	doi = {10.1016/j.ijbiomac.2015.08.055},
	language = {en},
	urldate = {2022-09-15},
	journal = {International Journal of Biological Macromolecules},
	author = {Li, Hongliang and Wu, Fan and Zhao, Lei and Tan, Jing and Jiang, Hongtao and Hu, Fuliang},
	month = nov,
	year = {2015},
	pages = {624--630},
	file = {Li et al. - 2015 - Neonicotinoid insecticide interact with honeybee o.pdf:C\:\\Users\\Utente\\Zotero\\storage\\SMG2FF53\\Li et al. - 2015 - Neonicotinoid insecticide interact with honeybee o.pdf:application/pdf},
}

@misc{us_epa_alternative_2017,
	type = {Other {Policies} and {Guidance}},
	title = {Alternative {Test} {Methods} and {Strategies} to {Reduce} {Vertebrate} {Animal} {Testing}},
	url = {https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/alternative-test-methods-and-strategies-reduce},
	abstract = {EPA's approach on new approach methodologies (NAMs)},
	language = {en},
	urldate = {2022-09-16},
	author = {US EPA, OCSPP},
	month = oct,
	year = {2017},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\JWSDV8XG\\alternative-test-methods-and-strategies-reduce.html:text/html},
}

@article{khalifa_overview_2021,
	title = {Overview of {Bee} {Pollination} and {Its} {Economic} {Value} for {Crop} {Production}},
	volume = {12},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2075-4450},
	url = {https://www.mdpi.com/2075-4450/12/8/688},
	doi = {10.3390/insects12080688},
	abstract = {Pollination plays a significant role in the agriculture sector and serves as a basic pillar for crop production. Plants depend on vectors to move pollen, which can include water, wind, and animal pollinators like bats, moths, hoverflies, birds, bees, butterflies, wasps, thrips, and beetles. Cultivated plants are typically pollinated by animals. Animal-based pollination contributes to 30\% of global food production, and bee-pollinated crops contribute to approximately one-third of the total human dietary supply. Bees are considered significant pollinators due to their effectiveness and wide availability. Bee pollination provides excellent value to crop quality and quantity, improving global economic and dietary outcomes. This review highlights the role played by bee pollination, which influences the economy, and enlists the different types of bees and other insects associated with pollination.},
	language = {en},
	number = {8},
	urldate = {2022-09-16},
	journal = {Insects},
	author = {Khalifa, Shaden A. M. and Elshafiey, Esraa H. and Shetaia, Aya A. and El-Wahed, Aida A. Abd and Algethami, Ahmed F. and Musharraf, Syed G. and AlAjmi, Mohamed F. and Zhao, Chao and Masry, Saad H. D. and Abdel-Daim, Mohamed M. and Halabi, Mohammed F. and Kai, Guoyin and Al Naggar, Yahya and Bishr, Mokhtar and Diab, Mohamed A. M. and El-Seedi, Hesham R.},
	month = aug,
	year = {2021},
	note = {Number: 8
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {bee visitation, bees pollination, challenges, crop production, economic, impact},
	pages = {688},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\KRKUK3X6\\Khalifa et al. - 2021 - Overview of Bee Pollination and Its Economic Value.pdf:application/pdf;Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\F22KMRZG\\htm.html:text/html},
}

@article{parish_evaluation_2020,
	title = {An evaluation framework for new approach methodologies ({NAMs}) for human health safety assessment},
	volume = {112},
	issn = {0273-2300},
	url = {https://www.sciencedirect.com/science/article/pii/S0273230020300180},
	doi = {10.1016/j.yrtph.2020.104592},
	abstract = {The need to develop new tools and increase capacity to test pharmaceuticals and other chemicals for potential adverse impacts on human health and the environment is an active area of development. Much of this activity was sparked by two reports from the US National Research Council (NRC) of the National Academies of Sciences, Toxicity Testing in the Twenty-first Century: A Vision and a Strategy (2007) and Science and Decisions: Advancing Risk Assessment (2009), both of which advocated for “science-informed decision-making” in the field of human health risk assessment. The response to these challenges for a “paradigm shift” toward using new approach methodologies (NAMS) for safety assessment has resulted in an explosion of initiatives by numerous organizations, but, for the most part, these have been carried out independently and are not coordinated in any meaningful way. To help remedy this situation, a framework that presents a consistent set of criteria, universal across initiatives, to evaluate a NAM's fit-for-purpose was developed by a multi-stakeholder group of industry, academic, and regulatory experts. The goal of this framework is to support greater consistency across existing and future initiatives by providing a structure to collect relevant information to build confidence that will accelerate, facilitate and encourage development of new NAMs that can ultimately be used within the appropriate regulatory contexts. In addition, this framework provides a systematic approach to evaluate the currently-available NAMs and determine their suitability for potential regulatory application. This 3-step evaluation framework along with the demonstrated application with case studies, will help build confidence in the scientific understanding of these methods and their value for chemical assessment and regulatory decision-making.},
	language = {en},
	urldate = {2022-09-16},
	journal = {Regulatory Toxicology and Pharmacology},
	author = {Parish, Stanley T. and Aschner, Michael and Casey, Warren and Corvaro, Marco and Embry, Michelle R. and Fitzpatrick, Suzanne and Kidd, Darren and Kleinstreuer, Nicole C. and Lima, Beatriz Silva and Settivari, Raja S. and Wolf, Douglas C. and Yamazaki, Daiju and Boobis, Alan},
	month = apr,
	year = {2020},
	keywords = {Evaluation framework, Human health, New approach methodologies, Risk assessment},
	pages = {104592},
	file = {ScienceDirect Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\BXDTX2WN\\Parish et al. - 2020 - An evaluation framework for new approach methodolo.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\NXFQ25LJ\\S0273230020300180.html:text/html},
}

@misc{development_new_nodate,
	title = {New {Approach} {Methodologies} ({NAMs}) and {Chemical} {Risk} {Assessment}},
	url = {https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=349550&Lab=CCTE},
	abstract = {The US EPA has made great advances in the development of new approach methodologies (NAMs) for filling information gaps for decision-making and integrating those tools and data streams into chemical risk assessment. NAMs are commonly defined to include in silico approaches, in chemico and in vitro assays, as well as the inclusion of information from the exposure of chemicals in the context of hazard assessment. They may also include a variety of new testing tools, such as 'high-throughput screening' and 'high-content methods' e.g. genomics, proteomics, metabolomics; as well as some 'conventional' methods that aim to improve understanding of toxic effects, either through improving toxicokinetic or toxicodynamic knowledge for substances. 

The EPA has worked with other stakeholders to leverage resources and develop NAMs that can support different regulatory contexts. Building confidence in the use of NAMs for regulatory decision-making is key to the increased implementation of these methods. The views expressed are those of the author and do not necessarily reflect the views of the US EPA. Presentation given to the Georgetown University Deptartment of Chemistry/Environmental Metrology and Policy Program class: Introduction to Environmental Health Risk Assessment and Management in April 2020 by Dr. Maureen Gwinn, Division Director of the US EPA Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division.},
	language = {en},
	urldate = {2022-09-16},
	author = {Development, Office of Research \&},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\UN8ARL5Z\\si_public_record_Report.html:text/html},
}

@article{ragoza_protein-ligand_2017,
	title = {Protein-{Ligand} {Scoring} with {Convolutional} {Neural} {Networks}},
	volume = {57},
	issn = {1549-960X},
	doi = {10.1021/acs.jcim.6b00740},
	abstract = {Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.},
	language = {eng},
	number = {4},
	journal = {Journal of Chemical Information and Modeling},
	author = {Ragoza, Matthew and Hochuli, Joshua and Idrobo, Elisa and Sunseri, Jocelyn and Koes, David Ryan},
	month = apr,
	year = {2017},
	pmid = {28368587},
	pmcid = {PMC5479431},
	keywords = {Ligands, Proteins, User-Computer Interface, Computational Biology, Protein Conformation, Drug Evaluation, Preclinical, Models, Molecular, Neural Networks, Computer},
	pages = {942--957},
	file = {Versione accettata:C\:\\Users\\Utente\\Zotero\\storage\\QPP8RBHT\\Ragoza et al. - 2017 - Protein-Ligand Scoring with Convolutional Neural N.pdf:application/pdf},
}

@article{koes_lessons_2013,
	title = {Lessons {Learned} in {Empirical} {Scoring} with smina from the {CSAR} 2011 {Benchmarking} {Exercise}},
	volume = {53},
	issn = {1549-9596},
	url = {https://doi.org/10.1021/ci300604z},
	doi = {10.1021/ci300604z},
	abstract = {We describe a general methodology for designing an empirical scoring function and provide smina, a version of AutoDock Vina specially optimized to support high-throughput scoring and user-specified custom scoring functions. Using our general method, the unique capabilities of smina, a set of default interaction terms from AutoDock Vina, and the CSAR (Community Structure–Activity Resource) 2010 data set, we created a custom scoring function and evaluated it in the context of the CSAR 2011 benchmarking exercise. We find that our custom scoring function does a better job sampling low RMSD poses when crossdocking compared to the default AutoDock Vina scoring function. The design and application of our method and scoring function reveal several insights into possible improvements and the remaining challenges when scoring and ranking putative ligands.},
	number = {8},
	urldate = {2022-10-01},
	journal = {Journal of Chemical Information and Modeling},
	author = {Koes, David Ryan and Baumgartner, Matthew P. and Camacho, Carlos J.},
	month = aug,
	year = {2013},
	note = {Publisher: American Chemical Society},
	pages = {1893--1904},
	file = {Versione accettata:C\:\\Users\\Utente\\Zotero\\storage\\3294PW9C\\Koes et al. - 2013 - Lessons Learned in Empirical Scoring with smina fr.pdf:application/pdf},
}

@article{eberhardt_autodock_nodate,
	title = {{AutoDock} {Vina} 1.2.0: new docking methods, expanded force field, and {Python} bindings},
	language = {en},
	author = {Eberhardt, Jerome and Santos-Martins, Diogo and Tillack, Andreas F and Forli, Stefano},
	pages = {7},
	file = {Eberhardt et al. - AutoDock Vina 1.2.0 new docking methods, expanded.pdf:C\:\\Users\\Utente\\Zotero\\storage\\BNJ2UH8N\\Eberhardt et al. - AutoDock Vina 1.2.0 new docking methods, expanded.pdf:application/pdf},
}

@article{morris_updated_nodate,
	title = {Updated for version 4.2.6},
	language = {en},
	author = {Morris, Garrett M and Goodsell, David S and Pique, Michael E and Huey, Ruth and Hart, William E and Halliday, Scott and Belew, Rik and Olson, Arthur J},
	pages = {69},
	file = {Morris et al. - Updated for version 4.2.6.pdf:C\:\\Users\\Utente\\Zotero\\storage\\4Z5TREAK\\Morris et al. - Updated for version 4.2.6.pdf:application/pdf},
}

@article{hochuli_visualizing_2018,
	title = {Visualizing convolutional neural network protein-ligand scoring},
	volume = {84},
	issn = {1093-3263},
	url = {https://www.sciencedirect.com/science/article/pii/S1093326318301670},
	doi = {10.1016/j.jmgm.2018.06.005},
	abstract = {Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques can make use of the increasing amounts of structural data that are becoming publicly available. Convolutional neural network (CNN) scoring functions in particular have shown promise in pose selection and affinity prediction for protein-ligand complexes. Neural networks are known for being difficult to interpret. Understanding the decisions of a particular network can help tune parameters and training data to maximize performance. Visualization of neural networks helps decompose complex scoring functions into pictures that are more easily parsed by humans. Here we present three methods for visualizing how individual protein-ligand complexes are interpreted by 3D convolutional neural networks. We also present a visualization of the convolutional filters and their weights. We describe how the intuition provided by these visualizations aids in network design.},
	language = {en},
	urldate = {2022-10-08},
	journal = {Journal of Molecular Graphics and Modelling},
	author = {Hochuli, Joshua and Helbling, Alec and Skaist, Tamar and Ragoza, Matthew and Koes, David Ryan},
	month = sep,
	year = {2018},
	keywords = {Deep learning, Molecular visualization, Protein-ligand scoring},
	pages = {96--108},
	file = {j.jmgm.2018.06.005.pdf:C\:\\Users\\Utente\\Zotero\\storage\\GUQMU5NJ\\j.jmgm.2018.06.005.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\G2IRZP2U\\S1093326318301670.html:text/html;Versione accettata:C\:\\Users\\Utente\\Zotero\\storage\\IR9KMM9L\\Hochuli et al. - 2018 - Visualizing convolutional neural network protein-l.pdf:application/pdf},
}

@article{onufriev_protonation_2013,
	title = {Protonation and {pK} changes in protein-ligand binding},
	volume = {46},
	issn = {0033-5835},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4437766/},
	doi = {10.1017/S0033583513000024},
	abstract = {Formation of protein-ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein-ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor-ligand binding affinities. Statistical prevalence, magnitude, and spatial distribution of the pK and protonation state changes in protein-ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface, but sometimes far away. When receptor-ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor-ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor-ligand interactions evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes – typically computed at the same pH for all complexes – may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.},
	number = {2},
	urldate = {2022-10-09},
	journal = {Quarterly reviews of biophysics},
	author = {Onufriev, Alexey V. and Alexov, Emil},
	month = may,
	year = {2013},
	pmid = {23889892},
	pmcid = {PMC4437766},
	pages = {181--209},
	file = {PubMed Central Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\WQ5YMSFL\\Onufriev e Alexov - 2013 - Protonation and pK changes in protein-ligand bindi.pdf:application/pdf},
}

@article{quiroga_vinardo_2016,
	title = {Vinardo: {A} {Scoring} {Function} {Based} on {Autodock} {Vina} {Improves} {Scoring}, {Docking}, and {Virtual} {Screening}},
	volume = {11},
	issn = {1932-6203},
	shorttitle = {Vinardo},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4865195/},
	doi = {10.1371/journal.pone.0155183},
	abstract = {Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.},
	number = {5},
	urldate = {2022-10-10},
	journal = {PLoS ONE},
	author = {Quiroga, Rodrigo and Villarreal, Marcos A.},
	month = may,
	year = {2016},
	pmid = {27171006},
	pmcid = {PMC4865195},
	pages = {e0155183},
	file = {PubMed Central Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\Y4TV2ITT\\Quiroga e Villarreal - 2016 - Vinardo A Scoring Function Based on Autodock Vina.pdf:application/pdf},
}

@book{camastra_machine_2015,
	address = {London},
	series = {Advanced {Information} and {Knowledge} {Processing}},
	title = {Machine {Learning} for {Audio}, {Image} and {Video} {Analysis}},
	isbn = {978-1-4471-6734-1 978-1-4471-6735-8},
	url = {http://link.springer.com/10.1007/978-1-4471-6735-8},
	urldate = {2022-10-19},
	publisher = {Springer},
	author = {Camastra, Francesco and Vinciarelli, Alessandro},
	year = {2015},
	doi = {10.1007/978-1-4471-6735-8},
	keywords = {Cluster Analysis, Image and Video Data, Machine Learning, Sequence Analysis, Signal Processing},
	file = {978-1-4471-6735-8_15.pdf:C\:\\Users\\Utente\\Zotero\\storage\\KJA2XQAL\\978-1-4471-6735-8_15.pdf:application/pdf;978-1-4471-6735-8_15.pdf:C\:\\Users\\Utente\\Zotero\\storage\\F8RBQIP4\\978-1-4471-6735-8_15.pdf:application/pdf;978-1-4471-6735-8_15.pdf:C\:\\Users\\Utente\\Zotero\\storage\\NVLJMVBW\\978-1-4471-6735-8_15.pdf:application/pdf;textbook.pdf:C\:\\Users\\Utente\\Zotero\\storage\\9LE4NWZK\\textbook.pdf:application/pdf;Versione inviata:C\:\\Users\\Utente\\Zotero\\storage\\KA8ZS3ZG\\Camastra e Vinciarelli - 2015 - Machine Learning for Audio, Image and Video Analys.pdf:application/pdf},
}

@article{kuntz_geometric_1982,
	title = {A geometric approach to macromolecule-ligand interactions},
	volume = {161},
	issn = {0022-2836},
	url = {https://www.sciencedirect.com/science/article/pii/002228368290153X},
	doi = {10.1016/0022-2836(82)90153-X},
	abstract = {We describe a method to explore geometrically feasible alignments of ligands and receptors of known structure. Algorithms are presented that examine many binding geometries and evaluate them in terms of steric overlap. The procedure uses specific molecular conformations. A method is included for finding putative binding sites on a macromolecular surface. Results are reported for two systems: the heme-myoglobin interaction and the binding of thyroid hormone analogs to prealbumin. In each case the program finds structures within 1 Å of the X-ray results and also finds distinctly different geometries that provide good steric fits. The approach seems well-suited for generating starting conformations for energy refinement programs and interactive computer graphics routines.},
	language = {en},
	number = {2},
	urldate = {2022-10-19},
	journal = {Journal of Molecular Biology},
	author = {Kuntz, Irwin D. and Blaney, Jeffrey M. and Oatley, Stuart J. and Langridge, Robert and Ferrin, Thomas E.},
	month = oct,
	year = {1982},
	pages = {269--288},
	file = {ScienceDirect Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\48R6UQYJ\\002228368290153X.html:text/html},
}

@incollection{liao_molecular_2013,
	series = {Future {Science} {Book} {Series}},
	title = {Molecular docking and structure-based virtual screening},
	url = {https://www.futuremedicine.com/doi/abs/10.4155/ebo.13.181},
	urldate = {2022-10-21},
	booktitle = {In {Silico} {Drug} {Discovery} and {Design}},
	publisher = {Future Science Ltd},
	author = {Liao, Chenzhong and Peach, Megan L and Yao, Risheng and Nicklaus, Marc C},
	month = oct,
	year = {2013},
	doi = {10.4155/ebo.13.181},
	pages = {6--20},
}

@article{meli_spyrmsd_2020,
	title = {spyrmsd: symmetry-corrected {RMSD} calculations in {Python}},
	volume = {12},
	issn = {1758-2946},
	shorttitle = {spyrmsd},
	url = {https://doi.org/10.1186/s13321-020-00455-2},
	doi = {10.1186/s13321-020-00455-2},
	abstract = {Root mean square displacement (RMSD) calculations play a fundamental role in the comparison of different conformers of the same ligand. This is particularly important in the evaluation of protein-ligand docking, where different ligand poses are generated by docking software and their quality is usually assessed by RMSD calculations. Unfortunately, many RMSD calculation tools do not take into account the symmetry of the molecule, remain difficult to integrate flawlessly in cheminformatics and machine learning pipelines—which are often written in Python—or are shipped within large code bases. Here we present a new open-source RMSD calculation tool written in Python, designed to be extremely lightweight and easy to integrate into existing software.},
	number = {1},
	urldate = {2022-10-22},
	journal = {Journal of Cheminformatics},
	author = {Meli, Rocco and Biggin, Philip C.},
	month = aug,
	year = {2020},
	keywords = {Software, Python, RMSD, Symmetry},
	pages = {49},
	file = {Full Text PDF:C\:\\Users\\Utente\\Zotero\\storage\\5MY98VGJ\\Meli e Biggin - 2020 - spyrmsd symmetry-corrected RMSD calculations in P.pdf:application/pdf;Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\4KZ3NVMC\\s13321-020-00455-2.html:text/html},
}

@misc{noauthor_rete_nodate,
	title = {Rete neurale convoluzionale},
	url = {https://it.mathworks.com/discovery/convolutional-neural-network-matlab.html},
	abstract = {Learn more about convolutional neural networks – what they are, why they matter, and how you can design, train, and deploy CNNs with MATLAB.},
	language = {it},
	urldate = {2022-10-29},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\PGNGRVVI\\convolutional-neural-network-matlab.html:text/html},
}

@misc{noauthor_reti_nodate,
	title = {Reti neurali, cosa sono? - {Applicazioni}, limiti ed evoluzione},
	shorttitle = {Reti neurali, cosa sono?},
	url = {https://www.intelligenzaartificiale.it/reti-neurali/},
	abstract = {Le reti neurali artificiali sono forme di intelligenza artificiale in grado di apprendere sfruttando meccanismi simili a quelli dell'intelligenza umana.},
	language = {it-IT},
	urldate = {2022-10-29},
	journal = {Intelligenza Artificiale},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\ZKY2VPSN\\reti-neurali.html:text/html},
}

@misc{noauthor_reti_2022,
	title = {Reti neurali: cosa sono, a cosa servono, tipi e storia},
	shorttitle = {Reti neurali},
	url = {https://www.ai4business.it/intelligenza-artificiale/deep-learning/reti-neurali/},
	abstract = {Cosa sono, come funzionano, con che tipo di chip possono funzionare le reti neurali e quali sono le loro nuove frontiere tecnologiche},
	language = {it-IT},
	urldate = {2022-10-29},
	journal = {AI4Business},
	month = apr,
	year = {2022},
	note = {Section: Deep Learning},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\MEZ2UGQM\\reti-neurali.html:text/html},
}

@misc{scitovation_what_2021,
	title = {What are {NAMs}?},
	url = {https://scitovation.com/what-are-nams/},
	abstract = {by Sage Corzine If you got pumped up about NAMs in a toxicology webinar, tried to find more information on Google, and got redirected to the North},
	language = {en-US},
	urldate = {2022-10-29},
	journal = {ScitoVation},
	author = {ScitoVation},
	month = jun,
	year = {2021},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\3B73PEYW\\what-are-nams.html:text/html},
}

@misc{us_epa_alternative_2017-1,
	type = {Other {Policies} and {Guidance}},
	title = {Alternative {Test} {Methods} and {Strategies} to {Reduce} {Vertebrate} {Animal} {Testing}},
	url = {https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/alternative-test-methods-and-strategies-reduce},
	abstract = {EPA's approach on new approach methodologies (NAMs)},
	language = {en},
	urldate = {2022-10-29},
	author = {US EPA, OCSPP},
	month = oct,
	year = {2017},
	file = {Snapshot:C\:\\Users\\Utente\\Zotero\\storage\\QYGMM4AE\\alternative-test-methods-and-strategies-reduce.html:text/html},
}