VECT-GAN: A variationally encoded generative model for overcoming data scarcity in pharmaceutical science

TL;DR

VECT-GAN is a novel generative model designed to augment small, noisy pharmaceutical datasets, significantly improving predictive model performance and enabling the development of new therapeutically relevant compounds.

Contribution

This paper introduces VECT-GAN, a variationally encoded conditional tabular GAN, with a pipeline for data augmentation that outperforms existing models and is pre-trained on ChEMBL for broader applicability.

Findings

VECT-GAN improves regression performance on pharmaceutical datasets.

Synthetic data regularizes small tabular datasets effectively.

Pre-trained VECT-GAN enhances generalisability to small molecule data.

Abstract

Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encoded Conditional Tabular Generative Adversarial Network (VECT-GAN), a novel generative model specifically designed for augmenting small, noisy datasets. We introduce a pipeline where data is augmented before regression model development and demonstrate that this consistently and significantly improves performance over other state-of-the-art tabular generative models. We apply this pipeline across six pharmaceutical datasets, and highlight its real-world applicability by developing novel polymers with medically desirable mucoadhesive properties, which we made and experimentally characterised. Additionally, we pre-train the model on the ChEMBL database of drug-like molecules, leveraging knowledge distillation to enhance its generalisability, making it readily available for use on pharmaceutical datasets containing small molecules, an extremely common pharmaceutical task. We demonstrate the power of synthetic data for regularising small tabular datasets, highlighting its potential to become standard practice in pharmaceutical model development, and make our method, including VECT-GAN pre-trained on ChEMBL available as a pip package.