Abstract
Designing selective and potent ligands for target receptors remains a significant challenge in drug discovery. Computational approaches, particularly advancements in machine learning (ML), offer transformative potential in addressing this challenge. In this study, our goal was to develop a composite ML model capable of predicting ligand selectivity and potency with high accuracy while also providing interpretable insights to guide ligand optimization. To achieve this goal, we first compiled a data set of 757 ligands, including metabotropic glutamate receptor subtype 2 (mGlu2) negative allosteric modulators (NAMs), metabotropic glutamate receptor subtype 3 (mGlu3) NAMs, and nonselective dual mGlu2/3 NAMs from patent filings. In three phases, we developed an ML model with Interpretable Graph Attention (I-GAT) networks for drug optimization. In phase 1, we created a composite model that can accurately predict selectivity and potency metrics by integrating graph architecture with transfer learning methodologies. Our model demonstrated over 97% accuracy in predicting ligand NAM selectivity and upward of 78% accuracy in potency prediction. Phase 2 used attention mechanisms to enhance model interpretability, effectively illuminating the "black box" of ML decision-making. Finally, in phase 3, we utilized attention gradients to intelligently modify known ligands, leading to the design of a novel ligand with predicted superior properties compared to the original. Our approach demonstrates the dual benefits of predictive accuracy and atom-level interpretability, offering a powerful framework for ligand design. When applied to external data, our model matched and, in some cases, exceeded the performance of current state-of-the-art chemistry-focused ML models across multiple data sets. Ultimately, our model has the potential to be adapted to other receptors and molecular properties, paving the way for a more efficient and targeted drug discovery process.