Exploring chemical compound space with quantum-based machine learning

By O. V. Lilienfeld, Klaus‐Robert Müller , Alexandre Tkatchenko. Nature Reviews Chemistry, 2019

330 citations recorded by the source corpus.

Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge. Machine-learning techniques have enabled, among many other applications, the exploration of molecular properties throughout chemical space. The specific development of quantum-based approaches in machine learning can now help us unravel new chemical insights.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/87da4c90c52b52eaae7aced8a9fbe17d28a27e9f

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Citations in the corpus

Machine Learning Methods in Drug Discovery (2020, background): “The chemical area is a relative set of descriptors, consisting of thousands of compounds within a frame with boundaries generated by ML algorithms [64,65].”
The MLIP package: moment tensor potentials with MPI and active learning (2020, background): “These are symbolic-regression potentials [29], non-conservative force fields [8,38], force-fields for a fixed-size molecular system (typically applied to one or two organic molecules) [9, 42], on-lattice potentials [66], and cheminformatics-type models [10, 14,19,21,28,30,62,64,71], see also a recen”
Higher-Order Explanations of Graph Neural Networks via Relevant Walks (2020, background): “In the field of machine learning for quantum chemistry [68], [69], [70], [71], [72], GNNs have been exhibiting state of the art performance for predicting molecular properties [3], [37],”