STELLA provides a drug design framework enabling extensive fragment-level chemical space exploration and balanced multi-parameter optimization.

ABSTRACT: In drug discovery, identifying molecules with desired pharmacological properties remains challenging, as conventional methods often rely on exhaustive trial-and-error and limited exploration of chemical space. Here, we present STELLA, a metaheuristics-based generative molecular design framework that combines an evolutionary algorithm for fragment-based chemical space exploration with a clustering-based conformational space annealing method for efficient multi-parameter optimization. Additionally, it leverages deep learning models for accurate prediction of pharmacological properties. Our case study, which focuses on docking score and quantitative estimate of drug-likeness as primary objectives, demonstrates that STELLA generates 217% more hit candidates with 161% more unique scaffolds and

SUBMITTER: Jeon H 

PROVIDER: S-EPMC12316942 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature