Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

Drug abuse (DA) or drug addiction is a complicated brain disorder which is commonly considered as neurobiological impairments caused by both genetic factors and environmental effects. Among DA-related targets, G protein-coupled receptors (GPCRs) play an important role in DA therapy. However, only 52 GPCRs have been published with crystal structures in the recent two decades. In the effort to overcome the limitations of crystal structure and conformational diversity of GPCRs, we built homology models and performed conformational searches by molecular dynamics (MD) simulation. To accelerate and facilitate the drug abuse research, we construct a DA-related GPCR-specific chemogenomics knowledgebase (KB) (DAKB-GPCRs) for its research that can be implemented with our established and novel chemogenomics tools as well as algorithms for data analysis and visualization. Our established TargetHunter and HTDocking tools, as well as our novel tools that include target classification and Spider Plot, are compiled into the platform. Our DAKB-GPCRs provides the following results for a query compound: (1) blood−brain barrier (BBB) plot via our BBB predictor, (2) docking scores via HTDocking, (3) similarity score via TargetHunter, (4) target classification via machine learning methods that utilize both docking scores and similarity scores, and (5) a drug−target interaction network via Spider Plot.