基于多目标优化的抗癌药物智能复方选择

Xiaoyan Liu, Zhiwei Xu, Guangwen Liu, Limin Liu
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引用次数: 0

摘要

在抗癌药物的化合物选择过程中,需要同时考虑药物活性和药代动力学等安全性。为了构建更完整、更精确的药物筛选机制,本文提出了一种基于多目标优化的抗癌药物智能化合物选择方法。提出的模型在MapReduce环境中执行。定量分析化合物的生物活性,定性分析其药代动力学性质和安全性(吸收、分布、代谢、排泄、毒性),建立多目标优化模型。在Pareto优化理论指导下,确定非劣解值集,通过遗传优化找到满足优化目标的复合组合。在此基础上,采用蒙特卡罗假设检验确定了化合物的装备范围。最后给出了抗乳腺癌药物的化合物选择实例,实验评价证明该算法能够有限筛选化合物,为抗乳腺癌药物的合成提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Compound Selection of Anti-cancer Drugs Based on Multi-Objective Optimization
In the compound selection process of anti-cancer drugs, safety properties such as drug activity and pharmacokinetics need to be considered simultaneously. To construct a more complete and precise drug screening mechanism, this paper proposed an intelligent compound selection method for anti-cancer drugs based on multi-objective optimization. The proposed model is executed in the MapReduce environment. Quantitatively analyze the biological activity of the compound, and qualitatively analyze the properties of pharmacokinetics and safety properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) to build a multi-objective optimization model. Guided by Pareto optimization theory, the set of non-inferior solution values was determined, and the compound combination that satisfies the optimization goal was found by genetic optimization. On this basis, a Monte Carlo hypothesis test was used to determine the equipped range of the compounds. Finally, an example of the compound selection of anti-breast cancer drugs is given, and the experimental evaluation proves that the algorithm can screen compounds limitedly, which provides a basis for anti-cancer drug synthesis.
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