From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

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Abstract

The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis. Artificial Intelligence and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data. This editorial highlights the main results presented during the special session of the International Conference on Neural Networks organized by “Big Data in Chemistry” project and draws perspectives on the future progress of the field.

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Molecular de-novo design through deep reinforcement learning

Marcus Olivecrona, Thomas Blaschke, Ola Engkvist … (2017)

This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model. Graphical abstract . Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0235-x) contains supplementary material, which is available to authorized users.

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Application of Generative Autoencoder in De Novo Molecular Design

Thomas Blaschke, Marcus Olivecrona, Ola Engkvist … (2017)

Abstract A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the trainings set were identified.

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Molecular representations in AI-driven drug discovery: a review and practical guide

Laurianne David, Amol Thakkar, Rocio Mercado … (2020)

The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.

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Contributors

Igor V. Tetko: itetko@vcclab.org

Journal

Journal ID (nlm-ta): J Cheminform

Journal ID (iso-abbrev): J Cheminform

Title: Journal of Cheminformatics

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 1758-2946

Publication date (Electronic): 18 December 2020

Publication date PMC-release: 18 December 2020

Publication date Collection: 2020

Volume: 12

Electronic Location Identifier: 74

Affiliations

[1 ]Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

[2 ]BIGCHEM GmbH, Valerystr. 49, 85716 Unterschleißheim, Germany

[3 ]GRID grid.418151.8, ISNI 0000 0001 1519 6403, Molecular AI, Discovery Sciences, R&D, AstraZeneca, ; Gothenburg, Sweden

Article

Publisher ID: 475

DOI: 10.1186/s13321-020-00475-y

PMC ID: 7747384

PubMed ID: 33339533

SO-VID: aa0752a3-4088-4a21-bba4-80d8f376046f

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Date received : 18 November 2020

Date accepted : 18 November 2020

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From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

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Most cited references 24

Molecular de-novo design through deep reinforcement learning

Application of Generative Autoencoder in De Novo Molecular Design

Molecular representations in AI-driven drug discovery: a review and practical guide

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