Showing posts from January, 2021

AI in Drug Discovery 2020 - A Highly Opinionated Literature Review

In this post, I present an annotated bibliography of some of the interesting machine learning papers I read in 2020.   Please don't be offended if your paper isn't on the list.  Leave a comment with other papers you think should be included. I've tried to organize these papers by topic.  Please be aware that the topics, selected papers, and the comments below reflect my own biases.  I've endeavored to focus primarily on papers that include source code .   Hopefully, this list reflects a few interesting trends I saw this year.  More of a practical focus on active learning Efforts to address model uncertainty, as well as the admission that it's a very difficult problem The (re)emergence of molecular representations that incorporate 3D structure Several interesting strategies for data augmentation Additional efforts toward model interpretability, coupled with the acknowledgment that this is also a difficult problem The application of generative models to more practical