Nathan Brown 
Artificial Intelligence in Drug Discovery 

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

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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