Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology

Title:Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology

Volume: 18 Issue: 14

Author(s): Sonia Arrasate*Aliuska Duardo-Sanchez*

Affiliation:

• Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa,Spain

• Department of Public Law, Chair on Human Genome and Law, University of the Basque Country (UPV/EHU), 48940, Leioa,Spain

Keywords: Perturbation theory, Machine Learning, Organic synthesis, Carbolithiations, Drug Discovery, Protein Targets, New Materials, OECD, REACH, Regulatory issues.

Abstract: Machine Learning (ML) models are very useful to predict physicochemical properties of small organic molecules, proteins, proteomes, and complex systems. These methods may be useful to reduce the cost of research in terms of materials resources, time, and laboratory animal sacrifice. Recently different authors have reported Perturbation Theory (PT) methods combined with ML to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems and in technology as well. Here, we present one state-of- the-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. In this work, we also embrace an overview of regulatory issues for acceptance and validation of both: the Cheminformatics models, and the characterization of new Biomaterials. This is a main question in order to make scientific result self for humans and environment.

Cite this article as:

Arrasate Sonia *, Duardo-Sanchez Aliuska *, Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology, Current Topics in Medicinal Chemistry 2018; 18 (14) . https://dx.doi.org/10.2174/1568026618666180810124031