A flexible data-free framework for structure-based de novo drug design with reinforcement learning.

ABSTRACT: Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prev

SUBMITTER: Du H 

PROVIDER: S-EPMC10631243 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature