In silico generation of novel, drug-like chemical matter using the LSTM neural network

By Peter Ertl, Richard A. Lewis, Eric J. Martin, Valery R. Polyakov. 2017

62 citations recorded by the source corpus.

The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural networks can provide a viable alternative to brute-force de novo approaches or various other machine-learning techniques for generating novel drug-like molecules. In this article we present a method to generate molecules using a long short-term memory (LSTM) neural network and provide an analysis of the results, including a virtual screening test. Using the network one million drug-like molecules were generated in 2 hours. The molecules are novel, diverse (contain numerous novel chemotypes), have good physicochemical properties and have good synthetic accessibility, even though these qualities were not specific constraints. Although novel, their structural features and functional groups remain closely within the drug-like space defined by the bioactive molecules from ChEMBL. Virtual screening using the profile QSAR approach confirms that the potential of these novel molecules to show bioactivity is comparable to the ChEMBL set from which they were derived. The molecule generator written in Python used in this study is available on request.

Subfields: Computational Theory and Mathematics, Environmental Chemistry, Physical and Theoretical Chemistry

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Citations in the corpus

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018, background): “Recently, there has been signiﬁcant advances in applying deep learning models to molecule generation [15, 38, 7, 9, 22, 4, 31, 27, 34, 42].”
Constrained Bayesian Optimization for Automatic Chemical Design (2017, methodology): “ the ﬁrst work on the topicGómez-Bombarelli et al.[2018]. Current methods feature molecular representations such as SMILES [Janz et al.,2018,Segler et al.,2017,Blaschke et al.,2017,Skalic et al.,2019,Ertl et al., 2017,Lim et al.,2018,Kang and Cho,2018,Sattarov et al.,2019,Gupta et al.,2018,Harel and”