Target structural based de novo drug generation

De novo drug design explores new chemical space and is not limited by known chemical libraries, which has been considered as the "holy grail" of drug discovery. Previously we have developed LigBuilder software series that can design target binding compounds that can be synthesis reachable with preferable properties. LigBuilder has been used by many academic and commercial users, who reported successful design examples that have been experimentally verified. Along with the rapid development of deep learning methods, various new AI techniques were introduced into the drug design field. We are working on methods that can take advantage of both physical based and AI based models. Deep generative models have gained much attention in de novo drug design in recent years. However, directly generating binding molecules inside the target site remains difficult. We developed DeepLigBuilder that can directly design 3D molecules inside the target binding pocket, by combining a novel 3D deep generative network for drug-like molecules and an optimization module that performs Monte Carlo Tree Search. Compounds designed using DeepLigBuilder for several targets show promising activity in experimental studies.