Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design. Advances with deep learning, the growth of databases of molecules for virtual screening and improvements in computational power have supported the emergence of a new field of quantitative structure-activity relationship (QSAR) modelling applications that Tropsha et al. term 'deep QSAR'. This article discusses key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning, and the use of deep QSAR models in structure-based virtual screening.