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

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.