Bridging generative AI and synthesis-on-demand chemical spaces for structure-based de novo drug design
File(s)
Author(s)
Ichim, Cosmin
Tran-Nguyen, Viet-Khoa
Ballester, Pedro
Type
Conference Paper
Abstract
Generative AI offers a powerful alternative for de novo drug design but frequently produces molecules that lack viable synthetic routes. While synthesis-on-demand (SoD) chemical spaces provide access to trillions of readily synthesizable compounds, their rapid expansion has created a computational bottleneck for effective virtual screening (VS). This study proposes a bridging strategy to navigate these challenges by identifying structurally similar analogs of AI-generated leads within SoD chemical spaces. We evaluated five generative methods AutoGrow4, LS-MolGen, MoLeR, MORLD, and REINVENT2.0) targeting the KEAP1-NRF2 protein-protein interaction. Using the Analog Hunter tool, we searched six SoD spaces, including the 7-trillion-molecule eXplore library, to find accessible analogs of the top AI-generated leads. Overall, the workflow identified 1,701 synthesizable analogs, while being substantially less resource-intensive than VS and maintaining high chemical diversity, with the REINVENT2.0-eXplore pairing benefiting the most from this bridge strategy. Furthermore, 1037 of these analogs were predicted to be more potent than the reference inhibitor iKEAP1. These results show that bridging generative AI with chemical spaces can overcome the synthetic gap in de novo design, providing a scalable and efficient framework for discovering synthetically accessible and high-potency drug leads.
Date Acceptance
2026-06-19
Citation
Lecture notes in Bioinformatics
Publisher
Springer
Journal / Book Title
Lecture notes in Bioinformatics
Copyright Statement
Subject to copyright. This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Source
21st International Conference on Computational Intelligence for Bioinformatics and Biostatistics (CIBB 2026)
Publication Status
Accepted
Start Date
2026-09-02
Finish Date
2026-09-04
Coverage Spatial
Rome, Italy