Artificial Intelligence in Chemistry: Current Trends and Future Directions

Abstract

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

The transformational role of GPU computing and deep learning in drug discovery

“…The ever-improving price-to-performance ratio of GPU hardware, reliance of DL on GPU and wide adoption of DL in CADD in recent years are all evident from the fact that over 50% of all ' AI in chemistry' documents in CAS Content Collection have been published in the past 4 years (ref. 109 ). Furthermore, hybrid AI methods have been adopted that combine conventional molecular simulations with DL for fast and accurate screening of ultra-large chemical libraries approaching hundreds of billions of molecules.…”

Working at the interfaces of data science and synthetic electrochemistry

“…While single and multivariate linear regressions are powerful tools for understanding and correlating experimental data [ 11 ], these approaches tend to only represent gradients of chemical reactivity but do not describe the implicit complex reaction surface. Non-parametric algorithms are typically invoked in modeling complex relationships and have seen increasing use in electrochemistry [ 7 , 10 , 30 – 32 ]. In 2019, Modestino and coworkers demonstrated that an artificial neural network (ANN) could improve yield and selectivity in the electrochemical synthesis of adiponitrile via the hydrodimerization of acrylonitrile ( Fig.…”

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] ; they have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite being very promising 25,26 . Another application of ML technologies in drug discovery is during compound screening or hit/lead generation and optimization enabling a virtual screening platform that offers a quicker and cheaper alternative to classic testing of large compounds libraries 27,28 ; virtual screening can be generally classified in ligand-based or structure-based 28 .…”