Artificial Intelligence for Contemporary Chemistry Research
- 1 January 2020
- journal article
- review article
- Published by Shanghai Institute of Organic Chemistry in Acta Chimica Sinica
- Vol. 78 (12), 1366-1382
- https://doi.org/10.6023/a20070306
Abstract
Artificial intelligence (AI), especially the machine learning, is playing an increasingly important role in contemporary scientific research. Unlike the traditional computer program, machine learning can analyze a large number of data repeatedly and optimize its own model, a process which is called a "learning process". So that the AI can find the relationship underling the experiments from a large number of data, form a new model with better prediction and decisionmaking ability, and make an optimized strategy. The characteristics of chemical research just hit the strengths of machine learning. Chemical research often faces very complex material system and experimental process, so it is difficult to accurately analyze and making judgment through physical chemistry principles. Artificial intelligence can mine the correlation of massive experimental data generated in chemical experiments, help chemists make reasonable analysis and prediction, and therefore greatly accelerate the process of chemical research. This review presents the modern artificial intelligence method and its basic principles on solving chemical problems, by representative examples with specific machine learning algorithm. The application of artificial intelligence in chemical science is in a period of vigorous rise. Artificial intelligence has initially shown a powerful assist to chemical research. We hope this review can help more domestic chemical workers understand and use this powerful tool.Keywords
This publication has 48 references indexed in Scilit:
- DeepTox: Toxicity Prediction using Deep LearningFrontiers in Environmental Science, 2016
- Machine learning: Trends, perspectives, and prospectsScience, 2015
- Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like MoleculesJournal of Chemical Information and Modeling, 2013
- In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination methodComputers in Biology and Medicine, 2013
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the ElectronsPhysical Review Letters, 2010
- Glomerular activity patterns evoked by natural odor objects in the rat olfactory bulb are related to patterns evoked by major odorant componentsJournal of Comparative Neurology, 2009
- Permutationally invariant potential energy surfaces in high dimensionalityInternational Reviews in Physical Chemistry, 2009
- QSAR study of neuraminidase inhibitors based on heuristic method and radial basis function networkEuropean Journal of Medicinal Chemistry, 2008
- Generalized Neural-Network Representation of High-Dimensional Potential-Energy SurfacesPhysical Review Letters, 2007
- Classification of a Diverse Set of Tetrahymena pyriformis Toxicity Chemical Compounds from Molecular Descriptors by Statistical Learning MethodsChemical Research in Toxicology, 2006