Artificial intelligence: machine learning for chemical sciences

Abstract

Graphical abstract 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. 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.

Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids

“…Recently, ML and deep learning (DL) models have started to augment various aspects of MD simulations [43][44][45][46][47][48][49]. More specifically, in the context of using ML methods to predict slowly converging properties of liquids-of which shear viscosity is one-some initial advances have been made.…”

Materials Discovery With Machine Learning and Knowledge Discovery

“…Several review papers have discussed the landscape of materials discovery based on machine learning ( Juan et al, 2021 ; Gao et al, 2022 ; Karthikeyan and Priyakumar, 2022 ; Wang et al, 2022 ), in some cases providing references to available databases ( Gao et al, 2022 ). We mention a couple of examples by way of illustrating specific applications.…”

Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization

“…Recent architectural developments in deep neural networks (DNN) [ 1 ] allowed for new applications beyond its initial targets in image recognition and text processing. As evident from the recent boost in the number of relevant publications [ 2 ], the field of chemistry has proved a fruitful ground for the application of the algorithms originally developed for natural language processing (NLP) [ 3 ] and graph processing (GP) [ 4 ] purposes. Chemical objects emerged as the natural extension of these algorithms due to the common representation of molecules as SMILES (text representation) [ 5 ] or molecular graphs (from valence theory).…”