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

arXiv: https://arxiv.org/abs/2603.18957 · HTML: https://mlsanity.com/paper/2603.18957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 19 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance](https://mlsanity.com/paper/1907.06477/) (2019)
- [Bayesian Group Regularization in Generalized Linear Models with a Continuous Spike-and-Slab Prior](https://mlsanity.com/paper/2007.07021/) (2020)
- [Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models](https://mlsanity.com/paper/2505.05416/) (2025)
- [Poisson process factorization for modeling mutational processes along cancer genomes](https://mlsanity.com/paper/2510.26090/) (2025)

## Abstract (arXiv)

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.