Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
Nina Gubina, Andrei Dmitrenko, Gleb Vitalevich Solovev, Lyubov Yamshchikova, Oleg Petrov, Ivan Lebedev, Nikita Serov, Grigorii Kirgizov
Abstract
Co-crystallization is an accessible way to control physicochemical characteristics of organic crystals, which finds many biomedical applications. In this work, we present Generative Method for Co-crystal Design (GEMCODE), a novel pipeline for automated co-crystal screening based on the hybridization of deep generative models and evolutionary optimization for broader exploration of the target chemical space. GEMCODE enables fast *de novo* co-crystal design with target tabletability profiles, which is crucial for the development of pharmaceuticals. With a series of experimental studies highlighting validation and discovery cases, we show that GEMCODE is effective even under realistic computational constraints. Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEMCODE and discuss its potential in accelerating drug development.
BibTeX
@inproceedings{
gubina2024hybrid,
title={Hybrid Generative {AI} for De Novo Design of Co-Crystals with Enhanced Tabletability},
author={Nina Gubina and Andrei Dmitrenko and Gleb Vitalevich Solovev and Lyubov Yamshchikova and Oleg Petrov and Ivan Lebedev and Nikita Serov and Grigorii Kirgizov and Nikolay Nikitin and Vladimir Vinogradov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=G4vFNmraxj}
}