Synthetic Data from Diffusion Models Improve Drug Discovery Prediction

TL;DR

This paper introduces Syngand, a diffusion GNN model that generates synthetic drug data to address data sparsity in drug discovery, enhancing the ability to perform cross-dataset analyses.

Contribution

The paper presents a novel diffusion GNN model, Syngand, capable of generating ligand and pharmacokinetic data end-to-end for drug discovery applications.

Findings

Synthetic data improves downstream regression performance

Syngand effectively samples pharmacokinetic data for existing ligands

Initial results show promising efficacy in drug property prediction

Abstract

Artificial intelligence (AI) is increasingly used in every stage of drug development. Continuing breakthroughs in AI-based methods for drug discovery require the creation, improvement, and refinement of drug discovery data. We posit a new data challenge that slows the advancement of drug discovery AI: datasets are often collected independently from each other, often with little overlap, creating data sparsity. Data sparsity makes data curation difficult for researchers looking to answer key research questions requiring values posed across multiple datasets. We propose a novel diffusion GNN model Syngand capable of generating ligand and pharmacokinetic data end-to-end. We show and provide a methodology for sampling pharmacokinetic data for existing ligands using our Syngand model. We show the initial promising results on the efficacy of the Syngand-generated synthetic target property data on downstream regression tasks with AqSolDB, LD50, and hERG central. Using our proposed model and methodology, researchers can easily generate synthetic ligand data to help them explore research questions that require data spanning multiple datasets.