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| Title | Development of a random background to understand ligand optimization. |
|---|---|
| Journal, issue, pages | Nature, Year 2026 |
| Publish date | Sep 16, 2026 |
Authors | Xinyu Xu / Olivier Mailhot / Galen J Correy / Xi-Ping Huang / Joao M Braz / Da Shi / Karthik Srinivasan / Kara Zielinski / Yuliia Holota / Yuliia Kuziv / Christos Iliopoulos-Tsoutsouvas / Nathan D Levinzon / Yagmur U Doruk / Moira M Rachman / Morgan E Diolaiti / Maisie G V Stevens / Fangyu Liu / Katie L Holland / Harald Hübner / Jing Wang / Yujin Wu / Alan Ashworth / Alexandros Makriyannis / Yuqi Zhang / Yurii S Moroz / Peter Gmeiner / Robert Abel / Aashish Manglik / Allan I Basbaum / Bryan L Roth / James S Fraser / Brian K Shoichet / ![]() |
| PubMed Abstract | Ligand optimization is central to drug discovery, with hundreds of analogues often designed and synthesized between an initial hit and a therapeutic candidate. The efficiency of this process is ...Ligand optimization is central to drug discovery, with hundreds of analogues often designed and synthesized between an initial hit and a therapeutic candidate. 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, 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. |
External links | Nature / PubMed:42749805 |
| Methods | EM (single particle) / X-ray diffraction |
| Resolution | 0.97 - 2.8 Å |
| Structure data | EMDB-71718, PDB-9pln: EMDB-71719, PDB-9plo: ![]() PDB-14ab: ![]() PDB-14am: ![]() PDB-14an: ![]() PDB-14ao: ![]() PDB-14ap: ![]() PDB-7iiw: ![]() PDB-7iix: ![]() PDB-7iiy: ![]() PDB-7iiz: ![]() PDB-7ij0: ![]() PDB-7ij1: ![]() PDB-7ij2: ![]() PDB-7ij3: ![]() PDB-7ij4: ![]() PDB-7ij5: ![]() PDB-7ij6: ![]() PDB-7ij7: ![]() PDB-7ij8: ![]() PDB-7ij9: ![]() PDB-7ija: ![]() PDB-7ijb: ![]() PDB-7ijc: ![]() PDB-7ijd: ![]() PDB-7ije: ![]() PDB-7ijf: ![]() PDB-7ijg: ![]() PDB-7ijh: ![]() PDB-7iji: ![]() PDB-7ijj: ![]() PDB-7ijk: ![]() PDB-7ijl: ![]() PDB-7ijm: |
| Chemicals | ![]() PDB-1dbs: ![]() ChemComp-HOH: ![]() PDB-1dbl: ![]() PDB-1dbm: ![]() ChemComp-DMS: ![]() PDB-1dbn: ![]() PDB-1dbe: ![]() PDB-1dbd: ![]() ChemComp-RLU: ![]() PDB-1cjb: ![]() PDB-1cjc: ![]() PDB-1cjd: ![]() PDB-1cje: ![]() PDB-1cjf: ![]() PDB-1cjr: ![]() PDB-1cjs: ![]() PDB-1cjt: ![]() PDB-1cjv: ![]() PDB-1cjw: ![]() PDB-1cjx: ![]() PDB-1cjy: ![]()
PDB-1cjz: COMPLEX OF GS-ALPHA WITH THE CATALYTIC DOMAINS OF MAMMALIAN ADENYLYL CYCLASE: COMPLEX WITH BETA-L-2'',3''-DIDEOXYATP, MN, AND MG ![]() PDB-1cj0: ![]() PDB-1cj1: ![]() PDB-1cj3: ![]() PDB-1akg: ![]() PDB-1cj4: ![]() PDB-1cj5: ![]() PDB-1cj6: ![]() PDB-1cj7: ![]() PDB-1cj8: ![]() PDB-1cka: ![]() PDB-1ckb: ![]() PDB-1ckc: ![]() PDB-1ckd: ![]() PDB-1cke: ![]() PDB-1ckf: ![]() PDB-1ckh: ![]() PDB-1cki: ![]() PDB-1ckj: ![]() PDB-1ckk: ![]() PDB-1ckl: ![]() PDB-1ciu: |
| Source |
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Keywords | VIRAL PROTEIN / Macrodomain / ADP-ribose / SARS-CoV-2 / fragment-based drug discovery / MEMBRANE PROTEIN / Receptor / Drug / Complex / GPCR / G-protein / scFv16 |
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