Generative molecule evolution using 3D pharmacophore for efficient Structure-Based Drug Design

TL;DR

This paper introduces MEVO, an evolutionary framework that combines generative models and physics-based scoring to efficiently design high-affinity molecules for drug targets, especially where data is scarce.

Contribution

The paper presents MEVO, a novel framework integrating VQ-VAE, diffusion models, and evolutionary strategies to enhance structure-based drug design with limited data.

Findings

Successfully generated high-affinity binders validated by FEP.

Designed potent KRAS G12D inhibitors with comparable affinity to known drugs.

Demonstrated versatility and generalizability in ligand design.

Abstract

Recent advances in generative models, particularly diffusion and auto-regressive models, have revolutionized fields like computer vision and natural language processing. However, their application to structure-based drug design (SBDD) remains limited due to critical data constraints. To address the limitation of training data for models targeting SBDD tasks, we propose an evolutionary framework named MEVO, which bridges the gap between billion-scale small molecule dataset and the scarce protein-ligand complex dataset, and effectively increase the abundance of training data for generative SBDD models. MEVO is composed of three key components: a high-fidelity VQ-VAE for molecule representation in latent space, a diffusion model for pharmacophore-guided molecule generation, and a pocket-aware evolutionary strategy for molecule optimization with physics-based scoring function. This framework efficiently generate high-affinity binders for various protein targets, validated with predicted binding affinities using free energy perturbation (FEP) methods. In addition, we showcase the capability of MEVO in designing potent inhibitors to KRAS$^{\textrm{G12D}}$, a challenging target in cancer therapeutics, with similar affinity to the known highly active inhibitor evaluated by FEP calculations. With high versatility and generalizability, MEVO offers an effective and data-efficient model for various tasks in structure-based ligand design.