Practical Cheminformatics

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 Me...

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.  Asid...

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...