Differential Compound Prioritization via Bidirectional Selectivity Push with Power
Differential Compound Prioritization via Bidirectional Selectivity Push with Power
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DOI：
10.1021/acs.jcim.7b00552
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发表时间：
2017-12-01
影响因子：
5.6
通讯作者：
Ning, Xia
中科院分区：
文献类型：
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作者：
Liu, Junfeng;Ning, Xia
有效的硅化合物优先排序是在药物发现的早期阶段确定有希望的候选药物的关键步骤。当前化合物优先排序的计算方法通常侧重于基于一个属性对化合物进行排序，通常是相对于单个目标的活性。然而，化合物的选择性也是一个关键的性质，应该同时考虑，以尽量减少未来药物的不良副作用的可能性。本文提出了一种新的基于机器学习的差分复合优先排序方法dCPPP。该方法学习的化合物优先排序模型对活性化合物进行了较好的排序，同时通过双向选择性推送策略对选择性化合物进行了较好的排序。双向推动是通过推动力量来增强的，这种推动力量是由选择性化合物在多种生物测定中的排名差异决定的。我们的实验表明，与基线模型相比，新方法dCPPP在优先选择选择性化合物方面取得了显着改进。
Effective in silico compound prioritization is a critical step to identify promising drug candidates in the early stages of drug discovery. Current computational methods for compound prioritization usually focus on ranking the compounds based on one property, typically activity, with respect to a single target. However, compound selectivity is also a key property which should be deliberated simultaneously so as to minimize the likelihood of undesired side effects of future drugs. In this paper, we present a novel machine-learning based differential compound prioritization method dCPPP. This dCPPP method learns compound prioritization models that rank active compounds well, and meanwhile, preferably rank selective compounds higher via a bidirectional selectivity push strategy. The bidirectional push is enhanced by push powers that are determined by ranking difference of selective compounds over multiple bioassays. Our experiments demonstrate that the new method dCPPP achieves significant improvement on prioritizing selective compounds over baseline models.