Advances in de novo drug design: From conventional to machine learning methods

Abstract

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure‐based and ligand‐based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement‐learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencod-ers. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine‐learning methodologies and highlights hot topics for further de-velopment.