Artificial intelligence: machine learning for chemical sciences

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences.

Automated summary

Research in molecular sciences has experienced the ups and downs of Artificial Intelligence (AI)/Machine Learning (ML) methods, particularly artificial neural networks, in the past few decades. However, there has been a significant resurgence in the use of modern ML methods in scientific research in recent years. These methods have achieved remarkable success in computer vision, speech recognition, natural language processing (NLP), and have inspired chemists and biologists to apply them in natural sciences. The availability of high-performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has facilitated this surge. ML algorithms have been successfully applied in various domains of molecular sciences, providing faster and sometimes more accurate solutions compared to traditional methods.