通过机器学习预测和解释小胶质细胞毒性。

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL
Qing Liu, Dakuo He, Mengmeng Fan, Jinpeng Wang, Zeyu Cui, Hao Wang, Yan Mi, Ning Li, Qingqi Meng, Yue Hou
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引用次数: 0

摘要

改善小胶质细胞介导的神经炎症是开发治疗神经退行性疾病新药的重要策略。植物化合物是发现治疗神经退行性疾病药物的重要筛选目标。然而,由于植物化学物质的空间复杂性,在化合物筛选的早期阶段,既要评估化合物的有效性,又要避免混入细胞毒性物质变得尤为重要。传统的高通量筛选方法成本高、效率低。基于机器学习的计算模型为细胞毒性测定提供了一条新途径。本研究采用机器学习方法开发了小胶质细胞毒性分类器。首先,我们提出了一种基于分子murcko通用支架的数据拆分策略,在此条件下,将三种机器学习方法与三种分子表征方法相结合,构建了小胶质细胞毒性分类器,并通过预测准确率、平衡准确率、F1-score和Matthews相关系数对其进行了比较和评估。然后,针对高维度的分子指纹引入了递归特征消除与支持向量机(RFE-SVC)降维方法,进一步提高了模型的性能。在所有小神经胶质细胞毒性分类器中,特征选择后与 ECFP4 指纹相结合的 SVM(ECFP4-RFE-SVM)对测试集的分类准确度最高(ACC 为 0.99,BA 为 0.99,F1-score 为 0.99,MCC 为 0.97)。最后,在解释小胶质细胞毒性分类器时使用了 Shapley 加性解释(SHAP)方法,并将关键子结构智能识别为结构警报。实验结果表明,ECFP4-RFE-SVM 对小胶质细胞毒性具有可靠的分类能力,SHAP 不仅可以为小胶质细胞毒性预测提供合理解释,还可以为后续的分子细胞毒性修饰提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction and Interpretation Microglia Cytotoxicity by Machine Learning.

Prediction and Interpretation Microglia Cytotoxicity by Machine Learning.

Ameliorating microglia-mediated neuroinflammation is a crucial strategy in developing new drugs for neurodegenerative diseases. Plant compounds are an important screening target for the discovery of drugs for the treatment of neurodegenerative diseases. However, due to the spatial complexity of phytochemicals, it becomes particularly important to evaluate the effectiveness of compounds while avoiding the mixing of cytotoxic substances in the early stages of compound screening. Traditional high-throughput screening methods suffer from high cost and low efficiency. A computational model based on machine learning provides a novel avenue for cytotoxicity determination. In this study, a microglia cytotoxicity classifier was developed using a machine learning approach. First, we proposed a data splitting strategy based on the molecule murcko generic scaffold, under this condition, three machine learning approaches were coupled with three kinds of molecular representation methods to construct microglia cytotoxicity classifier, which were then compared and assessed by the predictive accuracy, balanced accuracy, F1-score, and Matthews Correlation Coefficient. Then, the recursive feature elimination integrated with support vector machine (RFE-SVC) dimension reduction method was introduced to molecular fingerprints with high dimensions to further improve the model performance. Among all the microglial cytotoxicity classifiers, the SVM coupled with ECFP4 fingerprint after feature selection (ECFP4-RFE-SVM) obtained the most accurate classification for the test set (ACC of 0.99, BA of 0.99, F1-score of 0.99, MCC of 0.97). Finally, the Shapley additive explanations (SHAP) method was used in interpreting the microglia cytotoxicity classifier and key substructure smart identified as structural alerts. Experimental results show that ECFP4-RFE-SVM have reliable classification capability for microglia cytotoxicity, and SHAP can not only provide a rational explanation for microglia cytotoxicity predictions, but also offer a guideline for subsequent molecular cytotoxicity modifications.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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