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De novo Molecular Design with Generative Long Short-term Memory

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Drug discovery benefits from computational models aiding the identification of new chemical matter with bespoke properties. The field of de novo drug design has been particularly revitalized by adaptation of generative machine learning models from the field of natural language processing. These deep neural network models are trained on recognizing molecular structures and generate new molecular entities without relying on pre-determined sets of molecular building blocks and chemical transformations for virtual molecule construction. Implicit representation of chemical knowledge provides an alternative to formulating the molecular design task in terms of the established, explicit chemical vocabulary. Here, we review de novo molecular design approaches from the field of 'artificial intelligence', focusing on instances of deep generative models, and highlight the prospective application of long short-term memory models to hit and lead finding in medicinal chemistry.
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Keywords: CHEMOINFORMATICS; DEEP LEARNING; DRUG DISCOVERY; LSTM; NEURAL NETWORK

Document Type: Research Article

Affiliations: 1: ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland 2: ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland;, Email: [email protected]

Publication date: December 1, 2019

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  • International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions

    CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.

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