Herrera-Ibatá Diana M
Fundacion Universitaria Agraria de Colombia, Uniagraria, Facultad de Medicina Veterinaria, Bogota 111166, Colombia.
Curr Top Med Chem. 2021;21(7):649-660. doi: 10.2174/1568026621666210121153413.
Recently, different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing significant amounts of chemical data obtaining outcomes to find potential drug candidates.
最近，不同的作者报道了将微扰理论（PT）方法与机器学习（ML）相结合以获得PTML（PT + ML）模型。他们已将PTML模型应用于不同生物系统的研究。在此，我们对PTML模型在有机合成、药物化学、蛋白质研究和技术方面的不同应用进行了一项最新综述。这些模型的目的是找到分子描述符与生物学特征之间的关系，以预测新化合物的关键特性。机器学习非常有用的一个领域是药物发现过程。药物发现的整个过程会产生大量数据，而且这也是一个成本高昂且耗时的过程。机器学习带来了分析大量化学数据以获得结果从而找到潜在药物候选物的机会。
