Hot-Spot-Guided Generative Deep Learning for Drug-Like PPI Inhibitor Design.

Abstract

Protein-protein interactions (PPIs) are essential therapeutic targets, yet their large and relatively flat interfaces hinder the development of small-molecule inhibitors. Traditional computational approaches rely heavily on existing chemical libraries or expert heuristics, restricting exploration of novel chemical space. To address these challenges, we present Hot2Mol, a generative deep learning framework for the de novo design of target-specific and drug-like PPI inhibitors. Hot2Mol captures crucial pharmacophoric features from hot-spot residues, allowing precise targeting of PPI interfaces while eliminating the need for known bioactive ligands. The framework integrates three main components: a conditional transformer for pharmacophore-guided, property-constrained molecular generation; an E(n)-equivariant graph neural network to ensure accurate spatial alignment with PPI hot-spot pharmacophores; a variational autoencoder to sample novel and diverse molecular structures. Comprehensive assessments demonstrate that Hot2Mol outperforms state-of-the-art models in binding affinity, drug-likeness, synthetic accessibility, novelty, and uniqueness. Molecular dynamics simulations further confirm the strong binding stability of generated compounds. Case studies underscore Hot2Mol's ability to design high-affinity and selective PPI inhibitors, highlighting its potential to accelerate rational PPI-targeted drug discovery.