| Autor: |
Sergei Evteev, Yan Ivanenkov, Ivan Semenov, Maxim Malkov, Olga Mazaleva, Artem Bodunov, Dmitry Bezrukov, Denis Sidorenko, Victor Terentiev, Alex Malyshev, Bogdan Zagribelnyy, Anastasia Korzhenevskaya, Alex Aliper, Alex Zhavoronkov |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Frontiers in Chemistry, Vol 12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-2646 |
| DOI: |
10.3389/fchem.2024.1382512 |
| Popis: |
Introduction: 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.Methods: 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.Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm.Discussion: 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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