药物发现
计算机科学
数据科学
多样性(政治)
计算生物学
生物信息学
生物
社会学
人类学
作者
Dionisio A. Olmedo,Armando A. Durant-Archibold,José Luis López‐Pérez,José L. Medina‐Franco
出处
期刊:Combinatorial Chemistry & High Throughput Screening
[Bentham Science]
日期:2023-07-06
卷期号:27 (4): 502-515
被引量:6
标识
DOI:10.2174/1386207326666230705150110
摘要
Abstract: Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.
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