publications formating

_bibliography/papers.bib

@@ -72,7 +72,6 @@ @article{hruska2022ground
   abstract={Interpretation of chemistry on an atomic scale improves with explainable artificial intelligence (XAI). The parts of the molecule with the most significant influence on the chemical property of interest can be visualized with atomwise and bondwise attributions. Nonetheless, the attributions from different XAI methods regularly disagree substantially, causing uncertainty about which explanation is correct. To determine a ground truth for attributions, we define chemical operations which avoid alchemical steps or approximations and allow extracting one attribution per atom or bond from existing datasets of chemical properties. This general procedure allows for generating large datasets of ground truth attributions. The approach allowed us to create a ground truth explanation dataset with more than 5 million data points for the HOMO-LUMO gap chemical property. This open-source dataset of atomistic ground truth explanations may serve as a reference for XAI approaches.},
   preview={atomwise6.png},
   altmetric={True},
-  bibtex_show={true},
 }
 
 @article{suwala2024docking,
@@ -82,5 +81,5 @@ @article{suwala2024docking
   journal = {Preprint},
   preprint={https://chemrxiv.org/engage/chemrxiv/article-details/67470b577be152b1d00cfc8f},
   abstract={In this work, we benchmark 4 selected open source docking engines for use in the cytochrome P450 protein family. The key enzymes family of phase I metabolism is characterized by a wide variety of accepted substrates due to flexible active site. This work is a benchmark study which aims to evaluate the capabilities of current rigid and induced-fit docking methods for prediction of correct heme-ligand orientation. To asses it, we use two unique distances to heme iron and a SuCOS score to quantify reconstruction of orientation and chemical features. We selected three rigid protein docking engines: GNINA, AutoDock VINA, GalaxyDock2 HEME and a flexible docking model, RosettaFold-All-Atoms to test them on a dataset of 128 CYP-binding ligands. We report mean absolute error for RosetttaFold-All-Atom on key distance, to the atom closest to heme iron in experimental reference structure, 3 times lower than AutoDock VINA engine in the same simulation. Our results indicate that induced fit method is a significant improvement over rigid methods for flexible active site, but still offer limited predictivity. During crossdocking, RosettaFold-All-Atoms was able to recreate over a quarter of distances up to 20 percent difference from experiment. Further analysis indicates a low overlap in the distribution of ligand chemical features, based on a SuCOS score, which suggests a space for further improvement.},
-  preview={redocking-vs-crossdocking.png},
+  preview={crossdocking.PNG},
 }

_layouts/bib.html

@@ -2,7 +2,7 @@
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@@ -23,7 +23,7 @@
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