Accelerating drug discovery targeting dihydroorotate dehydrogenase using machine learning and generative AI approaches

Abstract

Dihydroorotate dehydrogenase (DHODH) is a key enzyme in pyrimidine biosynthesis, making it an attractive drug target for cancer, autoimmune diseases, and infections. Traditional DHODH inhibitor discovery is slow and costly. Our study integrated machine learning (ML) and generative artificial intelligence (AI) to accelerate this process, enhancing efficiency and reducing costs. We employed Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR) to predict pIC50 values, with RF achieving the highest accuracy (93 % test accuracy, 81 % on unseen molecules), demonstrating superior generalization. Using a Graph Convolutional Network-based Variational Autoencoder (GCN-VAE), we generated 59 unique drug-like molecules, five with pIC50 > 7, expanding the chemical space beyond conventional screening.

Docking studies confirmed strong binding affinities, with the most promising newly generated molecule showing a binding energy of –11.1 kcal/mol and an inhibition constant (Ki) of 269.8 nM. Key interactions with residues such as ALA59, PHE36, TYR38, GLN47, and ARG36 further validated stability and inhibitory potential. This AI-driven workflow accelerates DHODH inhibitor discovery by significantly reducing screening time, enhancing molecular diversity, and improving predictive accuracy. Our approach presents a scalable, cost-effective strategy for developing novel therapeutics, offering a transformative shift in drug discovery.