Advanced Computational Drug Discovery Unit, Institute of Innovative Research, Tokyo Institute of Technology, J3-23 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan.
Education Academy of Computational Life Sciences, Tokyo Institute of Technology, J3-141 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan.
Sci Rep. 2017 Sep 20;7(1):12038. doi: 10.1038/s41598-017-10275-4.
We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.
我们提出了一种新的迭代筛选竞赛方法来鉴定靶蛋白抑制剂。在 2014 年进行了一次化合物筛选竞赛后,我们在此研究中报告了 2015 年举行的竞赛的结果。我们的目的是鉴定靶酶抑制剂,并在相同的实验条件下对各种计算机辅助药物发现方法进行基准测试。在这两次竞赛中,我们均以酪氨酸蛋白激酶 Yes 作为靶蛋白的示例。参赛小组从一个包含 240 万种化合物的库中虚拟筛选可能的抑制剂。根据各自方法获得的功能评分对化合物进行排名,然后从每个小组中选择前 181 种化合物。与 2014 年竞赛的结果相比,我们在 2015 年竞赛中的命中率有所提高。此外,我们已经成功地确定了一种用于鉴定靶抑制剂的有统计学依据的方法。对最成功方法的定量分析为所用方法的重要特征提供了更多的见解。