An open unified deep graph learning framework for discovering drug leads

TL;DR

This paper introduces an open deep graph learning framework, GAFSE, that unifies multiple stages of drug lead discovery into one model, improving efficiency, interpretability, and success rates in drug development.

Contribution

It proposes a novel unified deep graph learning pipeline, GAFSE, integrating various drug discovery stages with standardized design and theoretical guarantees.

Findings

Achieves high performance across all lead discovery stages

Provides model interpretability and theoretical convergence guarantees

Streamlines drug discovery process reducing research overheads

Abstract

Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multi-stage multi-model integration. However, this is non-ideal, as clumsy integration of incompatible models increases research overheads, and may even reduce success rates in drug discovery. Facilitating compatibilities requires establishing inherent model consistencies across lead discovery stages. Towards that effect, we propose an open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework. GAFSE also offers standardized modular design and streamlined interfaces for future expansions and community support. GAFSE combines adversarial/generative learning, graph attention network, graph reconstruction network, and optimizes the classification/regression loss, adversarial/generative loss, and reconstruction loss simultaneously. Convergence analysis theoretically guarantees model generalization performance. Exhaustive benchmarking demonstrates that the GAFSE pipeline achieves excellent performance across almost all lead discovery stages, while also providing valuable model interpretability. Hence, we believe this tool will enhance the efficiency and productivity of drug discovery researchers.