2019/08/20 by Yibo Li, Li, Yibo, Jianxing Hu +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Chemical Synthesis and Analysis #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1908.07209
Updates to this version 1. Add supporting information (aux.pdf) 2. Improvements to Section 2.2 3. Resolve grammar issues
openalex publication_date 2019/08/20 · arxiv created 2019/09/05 · arxiv updated 2019/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.