DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning
This tool addresses the problem of efficient scaffold-based drug design for pharmaceutical researchers, representing an incremental improvement in molecular generation methods.
The researchers tackled scaffold-based drug discovery by developing DeepScaffold, a deep learning model that generates molecules retaining specified scaffold structures, and demonstrated its effectiveness through molecular docking evaluations on DRD2 targets.
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molecular generative model for scaffold-based drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores. Finally, a command line interface is created.