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

FSGLD, a multiscale DRUG-GAN generative lead discovery pipeline, identifies target-specific candidate compounds across three pharmacologically distinct target classes with encouraging performance.

• Why it matters: Drug lead discovery is costly and slow, and theoretical designs often fail to translate into viable candidates without bridging generative modeling and practical evaluation.
• What they did: FSGLD integrates generative modeling with molecular docking, molecular dynamics, ligand-residue interaction profiles, MM-PBSA, thermodynamic integration, and experimental validation, using DRUG-GAN across three generation scenarios.
• The result: A TI computational protocol reduces computation time by 80 to 90% while maintaining accuracy, enabling lower-cost identification of previously uncharacterized yet viable lead compounds.