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 amajor resurgence in the use of modern ML methods in scientific research during the last few years. Thesemethods have had phenomenal success in the areas of computer vision, speech recognition, natural languageprocessing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems innatural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, largedatasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been appliedto various domains in molecular sciences by providing much faster and sometimes more accurate solutionscompared 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 MLmethods 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 articleintends to conceptually introduce various modern ML methods and their relevance and applications incomputational natural sciences.

Synopsis Recent surge in the application of machine learning (ML) methods in fundamental sciences has led to a perspective that these methods may become important tools in chemical science. This perspective provides an overview of the modern ML methods and their successful applications in chemistry during the last few years.