Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory

Abstract

Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.

AI in computational chemistry through the lens of a decade-long journey

“…[136][137][138][139][140] An increased interest is also now in creating models directly predicting activation energies important for reaction design. 33,43,62,78,[141][142][143][144][145][146][147][148][149][150] Concerning reaction design and exploration, many specialized models are developed also for planning the synthesis and exploration of the reaction mechanisms. 33,43,62,78,[141][142][143][144][145][146][147][148][149][150][151] Spectroscopy Spectroscopy is one of the major applications of computational chemistry because it gives us a direct way to compare with experimental observables.…”

“…33,43,62,78,[141][142][143][144][145][146][147][148][149][150] Concerning reaction design and exploration, many specialized models are developed also for planning the synthesis and exploration of the reaction mechanisms. 33,43,62,78,[141][142][143][144][145][146][147][148][149][150][151] Spectroscopy Spectroscopy is one of the major applications of computational chemistry because it gives us a direct way to compare with experimental observables. The direct spectroscopy simulations with QM methods are often very time-consuming and, hence, AI offers itself as a promising solution to reduce the time cost.…”

State of the Art and of Outlook of Data Science and Machine Learning in Organic Chemistry

“…This field is rapidly adopting stateof-the-art ML algorithms and tools such as deep learning [49], tensors [50,51], and transformers [52,53] to solve and model chemical problems such as drug and polymer design, QSAR and QSPR studies on big data and huge datasets [54,55], and even to boost ab initio calculations [45,56]. As ML applications in chemistry are broad, open-source, and free frameworks and tools to assist chemists in developing ML models, such as OpenChem [57] and ML4Chem [58], have been designed. These frameworks encapsulate other frameworks, such as PyTorch and scikit-learn, making training and testing ML models in chemistry more straightforward.…”

Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge

“…24 Machine learning has become an increasing presence in the chemical space through drug screening efforts, conformational searches, and is used primarily in cheminformatics. 25,26 Pertaining to the SAMPL competitions, neural network approaches have been generated as a tool for understanding the quantitative structure-property relationships, or QSPR. 27,28 For SAMPL6, a deep learning approach with five hidden layers using extended connectivity fingerprints was constructed to predict the octanol-water partition coefficient (log P o/w ).…”