Modern machine learning for tackling inverse problems in chemistry: molecular design to realization

Abstract

candidate generation to their synthesis with a focus on small organic molecules. Optimization techniques like Bayesian optimization, reinforcement learning, attention-based transformers, deep generative models like variational autoencoders and generative adversarial networks form a robust arsenal of methods. This highlight summarizes the development of deep learning to tackle a wide variety of inverse design problems in chemistry towards the quest for synthesizing small organic compounds with a purpose.

Topics & Concepts