Nathan Brown 
Artificial Intelligence in Drug Discovery [PDF ebook] 

Introduction; The History of Artificial Intelligence and Chemistry; Chemical Topic Modelling – An Unsupervised Approach Originating from Text-mining to Organize Chemical Data; Deep Learning and Chemical Data; Concepts and Applications of Conformal Prediction in Computational Drug Discovery; Non-applicability Domain. The Benefits of Defining “I don’t know” in Artificial Intelligence; Predicting Protein-Ligand Binding-Affinities; Virtual Screening with Convolutional Neural Networks; Machine Learning in the Area of Molecular Dynamics Simulations; Compound Design Using Generative Neural Networks; Junction Tree Variational Autoencoder for Molecular Graph Generation; AI via Matched Molecular Pair Analysis; Molecular de novo Design Through Deep Generative Models; Active Learning for Drug Discovery and Automated Data Curation; Data-driven Prediction of Organic Reaction Outcomes; Chem OS: an Orchestration Software to Democratize Autonomous Discovery; Summary and Outlook