On the Selectivity of Generative Models in Structure-Based Drug Design

Ella Miray Rajaonson; Jungyoon Lee; William Chau; Alan Aspuru-Guzik; Benjamin Sanchez-Lengeling; Dominique Beaini; Kirill Neklyudov; Marta Skreta

arXiv · 2610.24124

On the Selectivity of Generative Models in Structure-Based Drug Design

Ella Miray Rajaonson·Jungyoon Lee·William Chau·Alan Aspuru-Guzik·Benjamin Sanchez-Lengeling·Dominique Beaini·Kirill Neklyudov·Marta Skreta

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Abstract

Designing small-molecule drugs that selectively bind to a target protein while avoiding unintended interactions with other proteins is essential for reducing side effects that contribute substantially to clinical attrition. Although structure-based drug design (SBDD) has advanced rapidly with deep learning, most generative models still optimize binding to a single target and disregard selectivity due to limited training data and unknown off-target interactions. Motivated by this gap, we introduce SELECTBENCH, a comprehensive benchmark assessing the selectivity of generative SBDD models. SELECTBENCH provides three curated evaluation tasks reflecting different real-world drug discovery scenarios: literature-based case studies, safety screening panels used in pharmaceutical development, and large-scale structure-based evaluation sets. We further introduce a suite of metrics to quantify selectivity and conduct a systematic evaluation of nine representative SBDD models, including methods explicitly trained for selectivity and widely-used inference-time guidance techniques. Our results show that even selectivity-trained models achieve only modest off-target discrimination and inference-time guidance provides limited gains, underscoring the need for method improvement. SELECTBENCH provides both a characterization of where current generative SBDD stands on selectivity and an open, extensible platform to accelerate progress. The benchmark is available at https://github.com/lovetaro/selectbench.

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Ella Miray Rajaonson, Jungyoon Lee, William Chau, Alan Aspuru-Guzik, Benjamin Sanchez-Lengeling, Dominique Beaini, Kirill Neklyudov, Marta Skreta. 2026-10-16. On the Selectivity of Generative Models in Structure-Based Drug Design. https://arxiv.org/abs/2610.24124

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