| Autor: |
José Teófilo Moreira-Filho, Meryck Felipe Brito da Silva, Joyce Villa Verde Bastos Borba, Arlindo Rodrigues Galvão Filho, Eugene N Muratov, Carolina Horta Andrade, Rodolpho de Campos Braga, Bruno Junior Neves |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Artificial Intelligence in the Life Sciences, Vol 3, Iss , Pp 100055- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2667-3185 |
| DOI: |
10.1016/j.ailsci.2022.100055 |
| Popis: |
Designing magic shotgun compounds, i.e., compounds hitting multiple targets using artificial intelligence (AI) systems based on machine learning (ML) and deep learning (DL) approaches, has a huge potential to revolutionize drug discovery. Such intelligent systems enable computers to create new chemical structures and predict their multi-target properties at a low cost and in a time-efficient manner. Most examples of AI applied to drug discovery are single-target oriented and there is still a lack of concise information regarding the application of this technology for the discovery of multi-target drugs or drugs with broad-spectrum action. In this review, we focus on current developments in AI systems for the next generation of automated design of multi-target drugs. We discuss how classical ML methods, cutting-edge generative models, and multi-task deep neural networks can help de novo design and hit-to-lead optimization of multi-target drugs. Moreover, we present state-of-the-art workflows and highlight some studies demonstrating encouraging experimental results, which pave the way for de novo drug design and multi-target drug discovery. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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