Affiliation:
1. Materials Research Laboratory (LEMat), Federal University of Mato Grosso, Campus Araguaia, Barra do Garcas, MT 78600-000,
Brazil.
Abstract
Abstract:
Data Science and Machine Learning approaches have recently expanded to accelerate
the discovery of new materials, drugs, synthetic substances and automated compound identification.
In the field of Organic Chemistry, Machine Learning and Data Science are commonly used
to predict biological and physiochemical properties of molecules and are referred to as quantitative
structure–active relationship (QSAR, for biological properties) and quantitative structure–
property relationship (QSPR, for nonbiological properties). Data Science and Machine Learning
applications are rapidly growing in chemistry and have been successfully applied to the discovery
and optimization of molecular properties, optimization of synthesis, automated structure elucidation,
and even the design of novel compounds. The main strength of Data Science tools is the ability to find
patterns and relationships that even an experienced researcher may not be able to find, and research in chemistry
can benefit from. Moreover, this interdisciplinary field is playing a central role in changing the way not only
organic chemistry but also how chemistry is done. As cutting-edge ML tools and algorithms such as tensors,
natural language processing, and transformers become mature and reliable by chemists. ML will be a routine
analysis in a chemistry laboratory like any other technique or equipment.
Publisher
Bentham Science Publishers Ltd.