Yang Jinn-Moon, Chen Yen-Fu, Shen Tsai-Wei, Kristal Bruce S, Hsu D Frank
Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 30050, Taiwan.
J Chem Inf Model. 2005 Jul-Aug;45(4):1134-46. doi: 10.1021/ci050034w.
Virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. The major weakness of virtual screening-the inability to consistently identify true positives (leads)-is likely due to our incomplete understanding of the chemistry involved in ligand binding and the subsequently imprecise scoring algorithms. It has been demonstrated that combining multiple scoring functions (consensus scoring) improves the enrichment of true positives. Previous efforts at consensus scoring have largely focused on empirical results, but they have yet to provide a theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening.
We demonstrate that combining multiple scoring functions improves the enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance and (b) the individual scoring functions are distinctive. Notably, these two prediction variables are previously established criteria for the performance of data fusion approaches using either rank or score combinations. This work, thus, establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in in silico screening experiments. Furthermore, it is similarly established that the second criterion (b) can, in at least some cases, be functionally defined as the area between the rank versus score plots generated by the two (or more) algorithms. Because rank-score plots are independent of the performance of the individual scoring function, this establishes a second theoretically defined approach to determining the likely success of combining data from different predictive algorithms. This approach is, thus, useful in practical settings in the virtual screening process when the performance of at least two individual scoring functions (such as in criterion a) can be estimated as having a high likelihood of having high performance, even if no training sets are available. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dihydrofolate reductase, and estrogen receptors of antagonists and agonists. Our procedure is computationally efficient, able to adapt to different situations, and scalable to a large number of compounds as well as to a greater number of combinations. Results of the experiment show a fairly significant improvement (vs single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rates, and "enrichment". This approach (available online at http://gemdock.life. nctu.edu.tw/dock/download.php) has practical utility for cases where the basic tools are known or believed to be generally applicable, but where specific training sets are absent.
对分子化合物库进行虚拟筛选是一种潜在的强大且经济的方法，可用于发现药物开发的新型先导化合物。虚拟筛选的主要弱点——无法始终如一地识别真正的阳性结果（先导物）——可能是由于我们对配体结合所涉及的化学过程理解不完整，以及随后评分算法不够精确。已经证明，组合多个评分函数（共识评分）可提高真正阳性结果的富集度。以往在共识评分方面的努力主要集中在实证结果上，但尚未提供理论分析，以深入了解虚拟筛选中组合和数据融合的实际特征。
我们证明，只有在以下情况下，组合多个评分函数才会提高真正阳性结果的富集度：（a）每个单独的评分函数都具有相对较高的性能；（b）各个评分函数具有独特性。值得注意的是，这两个预测变量是先前为使用排名或分数组合的数据融合方法的性能确立的标准。因此，这项工作为数据融合方法在提高计算机筛选实验产量方面可能取得成功建立了潜在的理论基础。此外，同样可以确定，在至少某些情况下，第二个标准（b）可以在功能上定义为两种（或更多）算法生成的排名与分数图之间的面积。由于排名 - 分数图与各个评分函数的性能无关，这确立了第二种理论定义的方法，用于确定组合来自不同预测算法的数据可能取得的成功。因此，当至少两个单独评分函数的性能（如标准a中所述）被估计很可能具有高性能时，即使没有可用的训练集，这种方法在虚拟筛选过程的实际应用中也很有用。我们使用来自五个评分系统的数据以及针对四个靶点（胸苷激酶、人二氢叶酸还原酶以及拮抗剂和激动剂的雌激素受体）的两种进化对接算法，对这种理论方法进行了初步验证。我们的程序计算效率高，能够适应不同情况，并且可扩展到大量化合物以及更多的组合。实验结果表明，在评分质量的几个指标上，特别是“命中优度”分数、假阳性率和“富集度”方面，与单一算法相比有相当显著的提高。这种方法（可在http://gemdock.life.nctu.edu.tw/dock/download.php在线获取）对于基本工具已知或被认为普遍适用但缺乏特定训练集的情况具有实际应用价值。
