Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Network with Group Lasso Regularization

• URL: http://arxiv.org/abs/2507.03318v1
• Date: Fri, 04 Jul 2025 06:12:18 GMT
• Title: Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Network with Group Lasso Regularization
• Authors: Zanyu Shi, Yang Wang, Pathum Weerawarna, Jie Zhang, Timothy Richardson, Yijie Wang, Kun Huang,
• Abstract summary: We build end-to-end explainable machine learning models for structure-activity relationship (SAR) modeling for compound property prediction.<n>We implement graph neural network (GNN) methods to obtain atom-level feature information and predict compound-protein affinity.<n>We also utilize group lasso and sparse group lasso to prune and highlight molecular subgraphs and enhance the structure-specific model explainability.
• Score: 11.595051456139021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable artificial intelligence (XAI) approaches have been increasingly applied in drug discovery to learn molecular representations and identify substructures driving property predictions. However, building end-to-end explainable machine learning models for structure-activity relationship (SAR) modeling for compound property prediction faces many challenges, such as limited activity data per target and the sensitivity of properties to subtle molecular changes. To address this, we leveraged activity-cliff molecule pairs, i.e., compounds sharing a common scaffold but differing sharply in potency, targeting three proto-oncogene tyrosine-protein kinase Src proteins (i.e., PDB IDs 1O42, 2H8H, and 4MXO). We implemented graph neural network (GNN) methods to obtain atom-level feature information and predict compound-protein affinity (i.e., half maximal inhibitory concentration, IC50). In addition, we trained GNN models with different structure-aware loss functions to adequately leverage molecular property and structure information. We also utilized group lasso and sparse group lasso to prune and highlight molecular subgraphs and enhance the structure-specific model explainability for the predicted property difference in molecular activity-cliff pairs. We improved drug property prediction by integrating common and uncommon node information and using sparse group lasso, reducing the average root mean squared error (RMSE) by 12.70%, and achieving the lowest averaged RMSE=0.2551 and the highest PCC=0.9572. Furthermore, applying regularization enhances feature attribution methods that estimate the contribution of each atom in the molecular graphs by boosting global direction scores and atom-level accuracy in atom coloring accuracy, which improves model interpretability in drug discovery pipelines, particularly in investigating important molecular substructures in lead optimization.

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