Guided Multi-objective Generative AI to Enhance Structure-based Drug Design

TL;DR

IDOLpro is a novel generative AI platform that combines diffusion models with multi-objective optimization to generate drug-like molecules with improved binding affinity and synthetic accessibility, significantly advancing structure-based drug design.

Contribution

The paper introduces IDOLpro, a new diffusion-based generative model that optimizes multiple physicochemical properties for drug discovery, outperforming existing methods in speed and effectiveness.

Findings

IDOLpro generates molecules with 10-20% better binding affinity.

IDOLpro produces more drug-like and synthetically accessible molecules.

IDOLpro is over 100x faster and less expensive than traditional virtual screening.

Abstract

Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in deep learning, existing models cannot generate molecules that satisfy all desired physicochemical properties. Herein, we describe IDOLpro, a generative chemistry AI combining diffusion with multi-objective optimization for structure-based drug design. Differentiable scoring functions guide the latent variables of the diffusion model to explore uncharted chemical space and generate novel ligands in silico, optimizing a plurality of target physicochemical properties. We demonstrate our platform's effectiveness by generating ligands with optimized binding affinity and synthetic accessibility on two benchmark sets. IDOLpro produces ligands with binding affinities over 10%-20% better than the next best state-of-the-art method on each test set, producing more drug-like molecules with generally better synthetic accessibility scores than other methods. We do a head-to-head comparison of IDOLpro against a classic virtual screen of a large database of drug-like molecules. We show that IDOLpro can generate molecules for a range of important disease-related targets with better binding affinity and synthetic accessibility than any molecule found in the virtual screen while being over 100x faster and less expensive to run. On a test set of experimental complexes, IDOLpro is the first to produce molecules with better binding affinities than experimentally observed ligands. IDOLpro can accommodate other scoring functions (e.g. ADME-Tox) to accelerate hit-finding, hit-to-lead, and lead optimization for drug discovery.