Showing posts from January, 2023

AI in Drug Discovery 2022 - A Highly Opinionated Literature Review

Here’s a roundup of some of the papers I found interesting in 2022. This list is heavily slanted to my interests, which lean toward the application of machine learning (ML) in drug design.  I’ve added commentary to most of the papers to explain why I found them compelling.  I’ve done my best to arrange the papers according to themes.  If I omitted a paper, please let me know.  I’d be happy to update this summary.   This review ended up being longer than I had anticipated, and there are several topics I didn’t cover.  If I have some time, this post may get a sequel.  Contents 1. Are Deep Neural Networks Better for QSAR? 2. Deep Learning Methods Provide New Approaches to Protein-Ligand Docking 3. Protein Structure Prediction - Pushing AlphaFold2 in New Directions 4. Model Interpretability 5. QM Methods 6. Utralarge Chemical Libraries 7. Active Learning 8. Molecular Representation 1. Are Deep Neural Networks Better for QSAR?   Based on papers I read and reviewed in 2022, there seems to be