# A Full-Spectrum Generative Lead Discovery (FSGLD) Pipeline via DRUG-GAN: A Multiscale Method for Drug-like/Target-specific Compound Library Generation

## TL;DR

This paper introduces a deep learning pipeline called FSGLD that efficiently generates drug-like and target-specific compounds, significantly improving lead discovery for drug development.

## Contribution

FSGLD integrates generative models with multiple computational and experimental techniques to enable efficient and accurate de novo drug design.

## Key findings

- FSGLD outperformed traditional methods in generating compounds targeting the CB2 receptor.
- A computational protocol reduced TI calculation time by 80–90% without sacrificing accuracy.
- The pipeline bridges theoretical design with practical validation, lowering lead discovery costs.

## 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 de novo design for both drug-like and target-specific compounds across three scenarios: I. generation of random drug-like compounds, II. generation of target-specific compounds, III. generation of target-biased compound series featuring shared chemical structures. FSGLD significantly outperformed traditional computer-aided drug design methods in generating novel chemicals which specifically target the CB2 receptor. Additionally, a computational protocol for TI calculations was established to reduce computation time by 80–90% while maintaining accuracy. By integrating generative models with in silico and in vitro evaluation techniques, FSGLD reduces the cost of identifying novel yet viable lead compounds, offering remarkable benefits to both academic and industry.