Artificial Intelligence for Computer-Aided Drug Discovery.

Autor:	Kate A; Amity Institute of Biotechnology, Amity University, Chhattisgarh, India., Seth E; Amity Institute of Biotechnology, Amity University, Chhattisgarh, India., Singh A; Amity Institute of Biotechnology, Amity University, Chhattisgarh, India., Chakole CM; Bajiraoji Karanjekar college of Pharmacy, Sakoli, Dist-Bhandara, India.; NDDS Research Lab, Delhi Institute of Pharmaceutical Sciences and Research, DPSR-University, New Delhi., Chauhan MK; NDDS Research Lab, Delhi Institute of Pharmaceutical Sciences and Research, DPSR-University, New Delhi., Singh RK; Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India., Maddalwar S; Amity Institute of Biotechnology, Amity University, Chhattisgarh, India., Mishra M; Amity Institute of Biotechnology, Amity University, Chhattisgarh, India.
Jazyk:	angličtina
Zdroj:	Drug research [Drug Res (Stuttg)] 2023 Sep; Vol. 73 (7), pp. 369-377. Date of Electronic Publication: 2023 Jun 05.
DOI:	10.1055/a-2076-3359
Abstrakt:	The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development. Competing Interests: All authors declare that they have no competing interest. (Thieme. All rights reserved.)
Databáze:	MEDLINE
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