Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations
Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations
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DOI：
10.1109/tcbb.2022.3155453
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Jingbo Yang-;Denan Zhang;Yiyang Cai;Kexin Yu;Mingming Li;Lei Liu;Xiujie Chen
中科院分区：
文献类型：
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作者：
Jingbo Yang-;Denan Zhang;Yiyang Cai;Kexin Yu;Mingming Li;Lei Liu;Xiujie Chen
识别药物表型效应，包括治疗效果和药物不良反应（ADR），是评价新药候选物（NDC）潜力不可分割的一部分。然而，目前用于预测NDC表型效应的计算方法主要基于NDC或相关靶标的整体结构。这些方法往往导致结构和功能之间的不一致，限制了NDC的预测空间。在这项研究中，首先，我们通过L1 SVM和L1 SVM机器学习模型构建了子结构域，域ADR和域ATC（解剖治疗化学分类系统代码）的定量关联。这些关联代表了表型（ADR和ATC）与药物和蛋白质的局部结构之间的关系。然后，基于这些建立的关联，构建子结构-表型关系，用于量化药物-表型关系。因此，这种方法可以实现高通量和有效的评价药物的NDC通过参考建立的亚结构-表型关系和结构信息的NDC没有额外的先验知识。使用该计算管道，总共预测了83，205种药物-ATC关系（包括1，479种药物和178种ATC）和306，421种药物-ADR关系（包括1，752种药物和454种ADR）。预测结果在四个水平上进行了验证：五重交叉验证，公共数据库，文献和分子对接。最后，通过三个案例验证了该方法的可行性。预计79例ATC和269例ADR与Maraviroc（获批药物）相关，包括临床使用中现有的抗病毒作用。此外，我们还发现了严重ADR的风险子结构，例如SUB 215（>= 1，饱和或仅芳香碳环大小为7）可导致休克。并根据已建立的药物-亚结构域-蛋白质间的相互作用，分析了感兴趣药物的作用机制。总之，该方法通过建立药物-亚结构-表型关系，可以实现对给定NDC或药物的表型的定量预测，而无需任何先验知识，除了其结构信息。该方法可以直接得到化合物的亚结构与表型之间的关系，便于分析药物的表型作用机制，加快药物的合理设计。
Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.