可药性
计算生物学
鉴定(生物学)
计算机科学
生物信息学
生物
遗传学
基因
植物
作者
Feng Zhu,Lianyi Han,Zheng Chen,Bin Xie,Martti T. Tammi,Shengyong Yang,Yuquan Wei,Yu Zong Chen
标识
DOI:10.1124/jpet.108.149955
摘要
Low target discovery rate has been linked to inadequate consideration of multiple factors that collectively contribute to druggability. These factors include sequence, structural, physicochemical, and systems profiles. Methods individually exploring each of these profiles for target identification have been developed, but they have not been collectively used. We evaluated the collective capability of these methods in identifying promising targets from 1019 research targets based on the multiple profiles of up to 348 successful targets. The collective method combining at least three profiles identified 50, 25, 10, and 4% of the 30, 84, 41, and 864 phase III, II, I, and nonclinical trial targets as promising, including eight to nine targets of positive phase III results. This method dropped 89% of the 19 discontinued clinical trial targets and 97% of the 65 targets failed in high-throughput screening or knockout studies. Collective consideration of multiple profiles demonstrated promising potential in identifying innovative targets.
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