Enhancing Unconditional Molecule Generation via Online Knowledge Distillation of Scaffolds.

ABSTRACT: Generating new drug-like molecules is an essential aspect of drug discovery, and deep learning models significantly accelerate this process. Language models have demonstrated great potential in generating novel and realistic SMILES representations of molecules. Molecular scaffolds, which serve as the key structural foundation, can facilitate language models in discovering chemically feasible and biologically relevant molecules. However, directly using scaffolds as prior inputs can introduce bias, thereby limiting the exploration of novel molecules. To combine the above advantages and address the limitation, we incorporate molecular scaffold information into language models via an Online knowledge distillation framework for the unconditional Molecule Generation task (

SUBMITTER: Wang H 

PROVIDER: S-EPMC11945111 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature