Title:Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology
Volume: 21
Issue: 7
Author(s): Diana M. Herrera-Ibatá*
Affiliation:
- Fundacion Universitaria Agraria de Colombia, Uniagraria, Facultad de Medicina Veterinaria, Bogota 111166,Colombia
Keywords:
Perturbation theory, Machine learning, Drug discovery, Protein targets, New materials, ChEMBL.
Abstract: 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.