AUTHOR=Evteev Sergei , Ivanenkov Yan , Semenov Ivan , Malkov Maxim , Mazaleva Olga , Bodunov Artem , Bezrukov Dmitry , Sidorenko Denis , Terentiev Victor , Malyshev Alex , Zagribelnyy Bogdan , Korzhenevskaya Anastasia , Aliper Alex , Zhavoronkov Alex 

TITLE=Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2024.1382512

DOI=10.3389/fchem.2024.1382512

ISSN=2296-2646

ABSTRACT=The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. In this study, we introduce Quantum-assisted Fragment-based Automated Structure GeneratorQuantum-based Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. The algorithm was evaluated for its ability to reproduce binding modes of known ligands for various pharmacological targets. Additionally, it successfully generated structures of new CAMKK2 and ATM inhibitors with low-micromolar activity. These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field.