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