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=12

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=<p><bold>Introduction:</bold> 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.</p><p><bold>Methods:</bold> We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors.</p><p><bold>Results:</bold> New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm.</p><p><bold>Discussion:</bold> 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.</p>