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...
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