posted on 2021-07-09, 16:38authored byBiao Ma, Kei Terayama, Shigeyuki Matsumoto, Yuta Isaka, Yoko Sasakura, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno
Recently,
molecular generation models based on deep learning have
attracted significant attention in drug discovery. However, most existing
molecular generation models have serious limitations in the context
of drug design wherein they do not sufficiently consider the effect
of the three-dimensional (3D) structure of the target protein in the
generation process. In this study, we developed a new deep learning-based
molecular generator, SBMolGen, that integrates a recurrent neural
network, a Monte Carlo tree search, and docking simulations. The results
of an evaluation using four target proteins (two kinases and two G
protein-coupled receptors) showed that the generated molecules had
a better binding affinity score (docking score) than the known active
compounds, and the generated molecules possessed a broader chemical
space distribution. SBMolGen not only generates novel binding active
molecules but also presents 3D docking poses with target proteins,
which will be useful in subsequent drug design. The code is available
at https://github.com/clinfo/SBMolGen.