化学空间
利用
药物发现
计算机科学
空格(标点符号)
生化工程
药品
进化算法
计算生物学
数据科学
人工智能
生物
生物信息学
工程类
药理学
操作系统
计算机安全
作者
Tu C. Le,David Alan Winkler
出处
期刊:ChemMedChem
[Wiley]
日期:2015-06-09
卷期号:10 (8): 1296-1300
被引量:44
标识
DOI:10.1002/cmdc.201500161
摘要
Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery.
科研通智能强力驱动
Strongly Powered by AbleSci AI