I‑GAT: Interpretable Graph Attention Networks for Ligand Optimization

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.

1

General overview of three phases of this study. Phase 1 focused on training the model to accurately predict ligand properties, including selectivity and potency. After the model was successfully trained, it was interrogated during phase 2 to identify the atoms or functional groups that negatively affect selectivity/potency. Subsequently, in phase 3, these regions were modified to optimize the ligand. The new ligand was evaluated to determine if the modifications improved the selectivity and/or potency of the ligand.

2

Simplified three-layer GAT model architecture tailored to task 1.

3

Confusion matrices are depicted for the selectivity classification task. (A) Numerical values for the 5-fold cross-validation set, (B) with values normalized across the “true” category for the 5-fold cross-validation set. (C) Numerical values for the independent validation set, (D) with values normalized across the “true” category for the independent validation set.

4

Simplified GAT model architecture tailored to task 2. The weights of the selectivity classification network were transferred onto the four-layer model subcomponents to “inform” the model.

5

Top graph depicts the regression output of the GAT model (task 2) comparing actual versus predicted normalized IC50 values after log transform, compiled across all five folds. The bottom graph depicts the regression output of the GAT model (task 2) comparing actual versus predicted normalized IC50 values after log transform for the independent validation data set.

6

Simplified GAT model architecture tailored to task 3. The weights of the selectivity classification network (left side) and of the potency regression network (right side) were transferred onto the model core. The MLP subcomponent (gray nodes) was used to process both data streams.

7

Confusion matrices are depicted for the potency classification task. (A) Numerical values for the 5-fold cross-validation set, (B) with values normalized across the “true” category for the 5-fold cross-validation set. (C) Numerical values for the independent validation set, (D) with values normalized across the “true” category for the independent validation set.

8

MGlu2 ligand A041 is analyzed for selectivity interpretability. (A) The original structure is depicted. (B) The resulting selectivity gradient evaluation reveals the three most “problematic” atoms, designated in purple, red, and orange highlights.

9

MGlu2 ligand A041 is analyzed for potency interpretability. (A) The original structure is depicted. (B) The resulting potency gradient evaluation reveals the most “problematic” atom, circumscribed in blue highlight.

10

Top panel depicts the effect of the structural modification without superimposed selectivity gradients; the bottom panel depicts the structures with superimposed potency gradients. The negatively performing region of the starting molecule, BOTH112, is highlighted in red. After the substitution of the nitrogen with the carbon atom (blue highlight), the target areas show a favorable gradient change. This correlated to a favorable change in predicted selectivity for the target area in ligand BOTH116.

11

Potency optimization for the ligand A273. The top panel depicts the effect of the structural modification without superimposed potency gradients; the bottom panel depicts the structures with superimposed potency gradients. The negatively performing region of the starting molecule, A273, is highlighted in red. After the substitution of the nitrile group with the amide group (blue highlight), the target areas show a favorable gradient change. This correlated to a favorable change in predicted potency for the target area in ligand A275.

12

Top panel depicts the effect of the structural modification without selectivity gradients; the bottom panel depicts the structures with superimposed selectivity gradients. The problem region of the ligand A053 is highlighted in red. After the substitution of the nitrile group with an amide group, the subsequent novel structure is analyzed for selectivity interpretability. The target area shows a favorable gradient change, indicating a favorable change in predicted selectivity for the “problem area”, now highlighted in green within the novel ligand, KE_01.

13

Top panel depicts the effect of the structural modification without potency gradients; the bottom panel depicts the structures with superimposed potency gradients. The problem region of the ligand A053 is highlighted in red. After the substitution of the nitrile group with an amide group, the subsequent novel structure is analyzed for potency interpretability. The target area shows a favorable gradient change, indicating a favorable change in predicted potency for the “problem area”, now highlighted in green within the novel ligand KE_01.