Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, USA.
Protein Sci. 2021 May;30(5):1087-1097. doi: 10.1002/pro.4065. Epub 2021 Mar 25.
EDock-ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock-ML takes a bottom-up approach in which machine-learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine-learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324-2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock-ML can be accessed freely at edock-ml.umsl.edu.
EDock-ML 是一个网络服务器，它使用集成对接和机器学习来帮助决定化合物是否值得在药物发现过程中进一步考虑。集成对接提供了一种经济的方法来考虑受体在分子对接中的灵活性。机器学习提高了对接评分的使用，以评估化合物是否可能有用。EDock-ML 采用自下而上的方法，一次开发一个蛋白质的机器学习模型，以提高其数据库中包含的蛋白质的预测能力。由于机器学习模型旨在在不改变与模型一起训练的对接和模型参数的情况下使用，因此新手用户可以直接使用它，而不必担心选择哪些参数。用户只需提交来自 ZINC 数据库的 ID 指定的化合物（Sterling，T.；Irwin，J. J.，J Chem Inf Model 2015，55[11]，2,324-2,337.），或上传由化学绘图程序准备的文件，并接收输出，帮助用户决定化合物对药物靶标是否具有活性或非活性的可能性。EDock-ML 可在 edock-ml.umsl.edu 上免费访问。
