Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.
Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.
Drug Discov Today Technol. 2019 Dec;32-33:89-98. doi: 10.1016/j.ddtec.2020.08.003. Epub 2020 Sep 20.
Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.
药物化学计量学是一种基于机器学习的建模方法，依赖于配体和蛋白质描述符的组合。随着机器学习的不断发展和公共数据的增加，该技术在药物早期发现中得到了更频繁的应用，通常用于配体-靶标结合预测。常见的应用包括改进单靶定量构效关系模型、蛋白质选择性和混杂性建模，以及大规模深度学习方法。在多靶生物活性建模中观察到使用药物化学计量学可以提高预测能力，为涵盖整个蛋白质家族的更广泛研究打开了大门。除此之外，随着深度学习推动更复杂和更大规模的模型，药物化学计量学允许更快和更高质量的计算模型，支持设计、制造、测试周期。
