标题： 预测设计的小生物分子的新功能角色：一种利用PubChem化合物和物质标识符的ML方法（CID-SID ML模型） 显示英文标题

标题： Predicting Novel Functional Roles of Designed Small Biomolecules: An ML Approach Utilizing PubChem Compound and Substance Identifiers (CID-SID ML model)

摘要： 意义与目标：所提出的方法旨在为药物发现的早期阶段提供一种省时且经济高效的途径。 本研究开发的机器学习模型仅使用了来自PubChem提供的标识符。 因此，获得PubChem CID和SID的药物开发研究人员可以轻松识别化合物的新功能。 通过四种生物测定方法展示了该方法，包括：(i)人D3多巴胺受体拮抗剂；(ii)Rab9激活剂促进剂； (iii)CHOP的小分子抑制剂以调节未折叠蛋白反应对内质网应激；(iv)人M1毒蕈碱受体拮抗剂。 解决方案：用于展示该方法的四种生物测定方法由PubChem提供。 对于每种生物测定方法，提取了由PubChem生成的CID、SID以及相应的活性。 所得数据集经过水溶性生物测定数据集筛选，仅保留两种生物测定中共同的化合物。 这样减少了非活性化合物的数量。 然后加入所有活性化合物，所得数据集随后用于基于scikit-learn算法的机器学习。 结果：四种生物测定中ML模型指标的平均值为：83.82% 准确率，标准差为5.35；87.9% 精确率，标准差为5.04；77.1% 召回率，标准差为7.65；82.1% F1分数，标准差为6.44；83.4% ROC曲线，标准差为5.09。 由于该方法作为预印本公开可用，又开发了四个机器学习模型。 它们的结果在“结果与讨论”部分进行了讨论。 显示英文摘要

摘要： Significance and Object: The proposed methodology aims to provide time- and cost-effective approach for the early stage in drug discovery. The machine learning models developed in this study used only the identification numbers provided by PubChem. Thus, a drug development researcher who has obtained a PubChem CID and SID can easily identify new functionality of their compound. The approach was demonstrated, using four bioassay which were on (i) the antagonists of human D3 dopamine receptors; (ii) the promoter Rab9 activators; (iii) small molecule inhibitors of CHOP to regulate the unfolded protein response to ER stress; (iv) antagonists of the human M1 muscarinic receptor. Solution: The four bioassays used for demonstration of the approach were provided by PubChem. For each bioassay, the generated by PubChem CIDs, SIDs were extracted together with the corresponding activity. The resulting dataset was sifted with the dataset on a water solubility bioassay, remaining only the compounds common for both bioassays. In this way, the inactive compounds were reduced. Then, all active compounds were added, and the resulted dataset was later used for machine learning based on scikit learn algorithms. Results: The average values of the ML models` metrics for the four bioassays were: 83.82% Accuracy with 5.35 standard deviation; 87.9% Precision with 5.04 standard deviation; 77.1% Recall with 7.65 standard deviation; 82.1% F1 with 6.44 standard deviation; 83.4% ROC with 5.09 standard deviation. Since the methodology was publicly available as a preprint, four more machine ML models have been developed. Their results are discussed in the "Results and Discussion" section.