BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery

2026-03-19

AI Summary

This work addresses the challenge in drug discovery posed by high-dimensional, noisy auxiliary features—such as chemical and genomic data—that exhibit heterogeneous relevance, thereby limiting both predictive performance and model interpretability. To tackle this issue, the study introduces a novel approach that, for the first time, integrates Bayesian variable selection into an inductive matrix completion framework. By learning sparse latent embeddings, the method automatically identifies and prioritizes the most informative biomedical features. Evaluated on tasks including Mycobacterium tuberculosis drug resistance prediction and drug repositioning, the proposed model consistently outperforms current state-of-the-art methods. Moreover, it successfully pinpoints clinically meaningful variables, achieving a favorable balance between predictive accuracy and interpretability.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recent advances in drug discovery have demonstrated that incorporating side information (e.g., chemical properties about drugs and genomic information about diseases) often greatly improves prediction performance. However, these side features can vary widely in relevance and are often noisy and high-dimensional. We propose Bayesian Variable Selection-Guided Inductive Matrix Completion (BVSIMC), a new Bayesian model that enables variable selection from side features in drug discovery. By learning sparse latent embeddings, BVSIMC improves both predictive accuracy and interpretability. We validate our method through simulation studies and two drug discovery applications: 1) prediction of drug resistance in Mycobacterium tuberculosis, and 2) prediction of new drug-disease associations in computational drug repositioning. On both synthetic and real data, BVSIMC outperforms several other state-of-the-art methods in terms of prediction. In our two real examples, BVSIMC further reveals the most clinically meaningful side features.

Problem

• drug discovery
• side information
• high-dimensional noise
• interpretability
• feature relevance

Innovation

• Bayesian variable selection
• inductive matrix completion
• drug discovery
• feature sparsity
• interpretable machine learning