A Full-Spectrum Generative Lead Discovery (FSGLD) pipeline via DRUG-GAN: A multiscale method for drug-like/target-specific compound library generation.

Authors

Beihong Ji, Matthew Brock, Yuhui Wu, Yuemin Bian, Xibing He, Yue Liu, Yixuan Hao, Haocheng Tang, Junmei Wang

Published in

Science advances. Volume 12. Issue 44. Pages eaei5257. Oct 30, 2026. Epub Oct 28, 2026.

Abstract

We present the Full-Spectrum Generative Lead Discovery (FSGLD), a deep learning-driven pipeline for efficient drug lead identification. FSGLD integrates generative modeling with molecular docking, molecular dynamics simulations, ligand-residue interaction profile, MM-PBSA, thermodynamic integration (TI), and experimental validation to bridge theoretical design and practical application. The core multiscale DRUG-GAN models enable generative model-guided discovery of drug-like and target-specific compounds across three scenarios: (i) generation of random drug-like compounds, (ii) generation of target-specific compounds, and (iii) generation of target-specific compound series featuring shared chemical structures. In our assessment across three pharmacologically distinct target classes, FSGLD demonstrates encouraging performance in identifying target-specific candidate compounds. In addition, a computational protocol for TI calculations was established to reduce computation time by 80 to 90% while maintaining accuracy. By integrating generative models with in silico and in vitro evaluation techniques, FSGLD reduces the cost of identifying previously uncharacterized yet viable lead compounds, offering remarkable benefits to both academic and industry.

PMID:
42950041
Bibliographic data and abstract were imported from PubMed on 29 Oct 2026.

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