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AI in Drug Discovery - A Highly Opinionated Literature Review (Part III)

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Following up on Part I and Part II, the third post in this series is a collection of review articles published in 2023 that I found helpful.  Property Prediction Machine Learning Methods for Small Data Challenges in Molecular Science https://pubs.acs.org/doi/full/10.1021/acs.chemrev.3c00189 Practical guidelines for the use of gradient boosting for molecular property prediction https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00743-7 Application of message passing neural networks for molecular property prediction https://www.sciencedirect.com/science/article/pii/S0959440X23000908?via%3Dihub Molecular Similarity Molecular Similarity: Theory, Applications, and Perspectives https://chemrxiv.org/engage/chemrxiv/article-details/655f59b15bc9fcb5c9354a43 Molecular Representation From intuition to AI: evolution of small molecule representations in drug discovery https://academic.oup.com/bib/article/25/1/bbad422/7455245 Docking and Scoring The Impact of Supervised Learning Method

AI in Drug Discovery - A Highly Opinionated Literature Review (Part II)

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Picking up where we left off in Part I , this post covers several other ML in drug discovery topics that interested me in 2023.  Some areas, like large language models, are new, and most of the work is at the proof-of-concept stage.  Others, like active learning, are more mature, and several groups are starting to explore nuances of the methods.   Here’s the structure of Part II.  4. Large Language Models 5. Active Learning 6. Federated Learning 7. Generative Models 8. Explainable AI 9. Other Stuff 4. Large Language Models The emergence of GPT-4 and ChatGPT brought considerable attention to large language models (LLMs) in 2023.  In November and December, several large pharmas held “AI Day” presentations featuring LLM applications for clinical trial data analysis. Many of these groups demonstrated the ability of LLMs to ingest large bodies of unstructured clinical data and subsequently generate tables and reports based on natural language queries.  Aside from some very brief demos on co

AI in Drug Discovery 2023 - A Highly Opinionated Literature Review (Part I)

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Here’s the first part of my review of some interesting machine learning (ML) papers I read in 2023.  As with the previous editions , this shouldn’t be considered a comprehensive review.  The papers covered here reflect my research interests and biases, and I’ve certainly overlooked areas that others consider vital.  This post is pretty long, so I've split it into three parts, with parts II and III to be posted in the next couple of weeks.    I. Docking, protein structure prediction, and benchmarking II. Large Language Models, active learning, federated learning, generative models, and explainable AI III. Review articles 2023 was a bit of a mixed bag for AI in drug discovery.  Several groups reported that the deep learning methods for protein-ligand docking weren’t quite what they were initially cracked up to be.  AlphaFold2 became pervasive, and people started to investigate, with mixed success, the utility of predicted protein structures.  There were reports of significant advanc

Some Thoughts on Biotech vs Pharma for Computational Chemists

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A recent editorial by Dean Brown in J Med Chem and follow-up posts by Keith Hornberger and Derek Lowe prompted me to think about how we train computational chemists and cheminformaticians for careers in drug discovery. It also brought to mind some unique differences between how computational chemistry is practiced in biotech and pharma. For those who haven’t read Dean Brown’s editorial and the subsequent reactions, I’d highly recommend them. In short, the authors focused on how medicinal chemists were trained in the past and how biotech and the growth of outsourcing are changing that model. Traditionally, most medicinal chemists received academic training in organic synthesis labs and then learned medicinal chemistry on the job from more experienced colleagues. Chemists would typically start at the bench and gradually transition to roles where they led groups and/or drug discovery project teams. With the rise of smaller biotechs and the advent of chemistry outsourcing, many me