Issue 27, 2024

Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

Abstract

Dual-target drug design has gained significant attention in the treatment of complex diseases, such as cancers and autoimmune disorders. A widely employed design strategy is combining pharmacophores to leverage the knowledge of structure–activity relationships of both targets. Unfortunately, pharmacophore combination often struggles with long and expensive trial and error, because the protein pockets of the two targets impose complex structural constraints. In this study, we propose AIxFuse, a structure-aware dual-target drug design method that learns pharmacophore fusion patterns to satisfy the dual-target structural constraints simulated by molecular docking. AIxFuse employs two self-play reinforcement learning (RL) agents to learn pharmacophore selection and fusion by comprehensive feedback including dual-target molecular docking scores. Collaboratively, the molecular docking scores are learned by active learning (AL). Through collaborative RL and AL, AIxFuse learns to generate molecules with multiple desired properties. AIxFuse is shown to outperform state-of-the-art methods in generating dual-target drugs against glycogen synthase kinase-3 beta (GSK3β) and c-Jun N-terminal kinase 3 (JNK3). When applied to another task against retinoic acid receptor-related orphan receptor γ-t (RORγt) and dihydroorotate dehydrogenase (DHODH), AIxFuse exhibits consistent performance while compared methods suffer from performance drops, leading to a 5 times higher performance in success rate. Docking studies demonstrate that AIxFuse can generate molecules concurrently satisfying the binding mode required by both targets. Further free energy perturbation calculation indicates that the generated candidates have promising binding free energies against both targets.

Graphical abstract: Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

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Article information

Article type
Edge Article
Submitted
05 1 2024
Accepted
09 6 2024
First published
13 6 2024
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2024,15, 10366-10380

Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

S. Chen, J. Xie, R. Ye, D. D. Xu and Y. Yang, Chem. Sci., 2024, 15, 10366 DOI: 10.1039/D4SC00094C

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