Development of a random background to understand ligand optimization

Abstract Ligand optimization is central to drug discovery, with hundreds of analogues often designed and synthesized between an initial hit and a therapeutic candidate 1,2 . The efficiency of this process is unclear, partly because there is no random background for optimization to compare against. Such a random background might emerge from systematic random small substitutions across starting ligands, measuring the likelihood of achieving a substantial improvement in affinity or potency, or other property by any single perturbation. Recent literature has suggested that perhaps 10% of analogues with minor modifications improve upon the potency of a parent by tenfold or more 3,4 , but this number is clouded by reporting bias, intentional improvement and inter-group variability. To begin to establish a background expectation for ligand optimization, here we systematically modified 18 lead molecules across six targets with single-atom changes; 257 compounds were synthesized. Unexpectedly, 11.3% of these random small perturbation analogues improved potency by tenfold or more. Conversely, they typically had worse in vitro pharmacokinetics. Although it was possible to find analogues where the potency increase compensated for inferior exposure and half-life, resulting in more potent compounds in vivo, overall, a frustrated landscape for ligand optimization is revealed. This study begins to establish a background expectation for ligand potency optimization and offers a simple strategy to do so. It also begins to quantify the challenges confronting the field in moving beyond in vitro potency.

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Publication Details

Journal
Nature
Published
2026-09-16
DOI
https://doi.org/10.1038/s41586-026-11013-5
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Development of a random background to understand ligand optimization

Karthik Srinivasan, Alexandros Makriyannis, Da Shi, Yurii S. Moroz et al.
Nature
Computational Drug Discovery Methods
article

Development of a random background to understand ligand optimization

Karthik Srinivasan, Alexandros Makriyannis, Da Shi, Yurii S. Moroz, Allan I. Basbaum, Bryan L. Roth, Brian K. Shoichet, Morgan E. Diolaiti, Robert Abel, Yuliia Kuziv, Aashish Manglik, G.J. Correy, James S. Fraser, Alan Ashworth, Katie L. Holland, Olivier Mailhot, Peter Gmeiner, Moira Rachman, João M. Bráz, Harald Hübner, Xi‐Ping Huang, Nathan D. Levinzon, Yuqi Zhang, Jing Wang, Fangyu Liu, Christos Iliopoulos‐Tsoutsouvas, Yuliia Holota, Yagmur U. Doruk, Kara Zielinski, Yujin Wu, Maisie G. V. Stevens, Xinyu Xu
article en

Abstract

Abstract Ligand optimization is central to drug discovery, with hundreds of analogues often designed and synthesized between an initial hit and a therapeutic candidate 1,2 . The efficiency of this process is unclear, partly because there is no random background for optimization to compare against. Such a random background might emerge from systematic random small substitutions across starting ligands, measuring the likelihood of achieving a substantial improvement in affinity or potency, or other property by any single perturbation. Recent literature has suggested that perhaps 10% of analogues with minor modifications improve upon the potency of a parent by tenfold or more 3,4 , but this number is clouded by reporting bias, intentional improvement and inter-group variability. To begin to establish a background expectation for ligand optimization, here we systematically modified 18 lead molecules across six targets with single-atom changes; 257 compounds were synthesized. Unexpectedly, 11.3% of these random small perturbation analogues improved potency by tenfold or more. Conversely, they typically had worse in vitro pharmacokinetics. Although it was possible to find analogues where the potency increase compensated for inferior exposure and half-life, resulting in more potent compounds in vivo, overall, a frustrated landscape for ligand optimization is revealed. This study begins to establish a background expectation for ligand potency optimization and offers a simple strategy to do so. It also begins to quantify the challenges confronting the field in moving beyond in vitro potency.

Nature
University of North Carolina at Chapel Hill (US), Northeastern University (US), University of California, San Francisco (US), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Taras Shevchenko National University of Kyiv (UA), Schrodinger (United States) (US), University of Utah (US), UCSF Helen Diller Family Comprehensive Cancer Center (US), Enamine (Ukraine) (UA)
National Institutes of Health, Defense Advanced Research Projects Agency, University of California, San Francisco, Advanced Research Projects Agency
Good health and well-being
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
DOIOpen Access PDFOpenAlex
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