Merge pull request #4 from swansonk14/fixes

Updating README with NMI paper information

README.md

@@ -1,10 +1,19 @@
 # SyntheMol: Generative AI for Drug Discovery
 
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/synthemol)](https://badge.fury.io/py/synthemol)
+[![PyPI version](https://badge.fury.io/py/synthemol.svg)](https://badge.fury.io/py/synthemol)
+[![Downloads](https://pepy.tech/badge/synthemol)](https://pepy.tech/project/synthemol)
+[![license](https://img.shields.io/github/license/swansonk14/synthemol.svg)](https://github.com/swansonk14/SyntheMol/blob/main/LICENSE.txt)
+
 SyntheMol is a generative AI method for designing structurally novel and diverse drug candidates with predicted bioactivity that are easy to synthesize.
 
 SyntheMol consists of a Monte Carlo tree search (MCTS) that explores a combinatorial chemical space consisting of molecular building blocks and chemical reactions. The MCTS is guided by a bioactivity prediction AI model, such as a graph neural network or a random forest. Currently, SyntheMol is designed to use 137,656 building blocks and 13 chemical reactions from the [Enamine REAL Space](https://enamine.net/compound-collections/real-compounds/real-space-navigator), which can produce over 30 billion molecules. However, SyntheMol can be easily adapted to use any set of building blocks and reactions.
 
-SyntheMol is described in a forthcoming paper in _Nature Machine Intelligence_, where we applied SyntheMol to design novel antibiotic candidates for the Gram-negative bacterium _Acinetobacter baumannii_. Full details for reproducing the results in the paper are provided in the [docs](docs) directory.
+SyntheMol is described in the following paper, where we applied SyntheMol to design novel antibiotic candidates for the Gram-negative bacterium _Acinetobacter baumannii_.
+
+Swanson, K., Liu, G., Catacutan, D. B., Arnold, A., Zou, J., Stokes, J. M. [Generative AI for designing and validating easily synthesizable and structurally novel antibiotics](https://www.nature.com/articles/s42256-024-00809-7). _Nature Machine Intelligence_, 2024.
+
+Full details for reproducing the results in the paper are provided in the [docs](docs) directory.
 
 
 ## Table of contents

docs/README.md

@@ -1,6 +1,8 @@
 # Documentation
 
-This directory contains instructions for reproducing the results in forthcoming paper in _Nature Machine Intelligence_.
+This directory contains instructions for reproducing the results in our paper:
+
+Swanson, K., Liu, G., Catacutan, D. B., Arnold, A., Zou, J., Stokes, J. M. [Generative AI for designing and validating easily synthesizable and structurally novel antibiotics](https://www.nature.com/articles/s42256-024-00809-7). _Nature Machine Intelligence_, 2024.
 
 The relevant data should be downloaded from [this Zenodo record](https://zenodo.org/doi/10.5281/zenodo.10257838), unzipped, and moved to `SyntheMol/data`.
 

synthemol/_version.py

@@ -1,6 +1,6 @@
 """Contains the version information for synthemol."""
 # major, minor, patch
-version_info = 1, 0, 1
+version_info = 1, 0, 2
 
 # Nice string for the version
 __version__ = '.'.join(map(str, version_info))