A linear interpretable model for drug target prediction
Year: 2026
Type: article
Abstract: MOTIVATION: Identifying drug targets is fundamental in drug development, both for discovering new therapies and for ensuring effective and safe treatments. Drug-target interactions (DTIs) have been predicted using machine learning approaches that integrate heterogeneous data; however, often these models are complex and lack interpretability. RESULTS: We investigate whether a simple, fully interpretable linear model can achieve competitive performance for DTI prediction. We propose Linear Interpretable Drug-Target Interaction (LI-DTI), a prediction model inspired by recommender systems. LI-DTI learns from different drug-drug and target-target similarity matrices and provides interpretable predictions as a linear combination of these similarity measures. We show that LI-DTI can recover DTIs even when drugs or targets have no previously known interactions, across multiple cross-validation settings. We further evaluate performance while mitigating potential bias arising from high chemical similarity between drugs or sequence similarity between targets. Finally, we assess LI-DTI in a prospective evaluation, training on DTIs present in DrugBank from 2011 and testing on interactions added through 2022. Across all evaluations, LI-DTI achieves state-of-the-art performance while producing interpretable predictions. For practical use, we provide a web-based tool that enables users to visualize individual LI-DTI predictions for DrugBank (2025) and inspect the biological evidence underlying them. Our results indicate that simple linear models with well-curated similarity features can deliver robust and interpretable DTI predictions, facilitating hypothesis generation and downstream experimental prioritization. AVAILABILITY AND IMPLEMENTATION: Code and data available at https://github.com/paccanarolab/LI-DTI. Web tool available at https://paccanarolab.org/lidtiweb/.
Source: Bioinformatics
Institutions: Royal Holloway University of London, Fundação Getulio Vargas
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,989
Awards: G3011078955, Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data, A GPU-based high performance system for discovering consensus domain architecture and functional annotation of protein families, New advances in insecticide resistance genomics: using Machine Learning to predict resistance phenotype from large-scale genomic data., G5741163287, G6160231813, Bilateral NSF/BIO-BBSRC - Translational landscape to link cell growth with proliferation in the root meristem, G6799420748
OpenAlex ID: W7204129561