research-article

De novo drug design using self attention mechanism

Authors:

Vedang Mandhana,

Rutuja TawareAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 8 - 12

https://doi.org/10.1145/3341105.3374024

Published: 30 March 2020 Publication History

Abstract

The search space involved in drug discovery is huge. Hence, measures are being taken to perform this search more efficiently with the help of machine learning. Generative models have tremendously increased efficiency of de novo design of drug molecules. This paper proposes to use self-attention mechanism to generate novel molecules through language modeling. Language modeling enables the generated molecules to have properties similar to the molecules present in the training set. Self-attention enables the model to identify the relevant context between the various elements generated in the molecular sequence. This takes care of the dependencies involved in molecular structures. In this generative approach, the molecules are represented using formal molecule notation known as SMILES and the Transformer-XL architecture is used for training and sampling of novel molecules. The Transformer-XL architecture is successful in modeling molecular sequences of variable length.

References

[1]

Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, and Llion Jones. 2018. Character-Level Language Modeling with Deeper Self-Attention. CoRR abs/1808.04444 (2018). arXiv:1808.04444 http://arxiv.org/abs/1808.04444

[2]

John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, and José Miguel Hernández-Lobato. 2019. A Model to Search for Synthesizable Molecules. CoRR abs/1906.05221 (2019). arXiv:1906.05221 http://arxiv.org/abs/1906.05221

[3]

Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher. 2019. Guacamol: benchmarking models for de novo molecular design. Journal of chemical information and modeling 59, 3 (2019), 1096--1108.

[4]

Zihang Dai, Zhilin Yang, Yiming Yang, William W Cohen, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019. Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860 (2019).

[5]

Yoav Goldberg. 2016. A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research 57 (2016), 345--420.

Digital Library

[6]

Rafael Gmez-Bombarelli, Jennifer N. Wei, David Duvenaud, Jos Miguel Hernndez-Lobato, Benjamn Snchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Aln Aspuru-Guzik. 2018. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science 4, 2 (2018), 268--276. arXiv:https://doi.org/10.1021/acscentsci.7b00572 29532027.

[7]

Artur Kadurin, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov, and Alex Zhavoronkov. 2017. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 8, 7 (2017), 10883.

[8]

Egor Kraev and Mark Harley. 2019. Probabilistic hypergraph grammars for efficient molecular optimization. arXiv preprint arXiv:1906.01845 (2019).

[9]

Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt. 2018. Constrained Graph Variational Autoencoders for Molecule Design. CoRR abs/1805.09076 (2018). arXiv:1805.09076 http://arxiv.org/abs/1805.09076

[10]

Daniil Polykovskiy, Alexander Zhebrak, Dmitry Vetrov, Yan Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine Bozdaganyan, Alexander Aliper, Alex Zhavoronkov, and Artur Kadurin. 2018. Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. Molecular Pharmaceutics 15, 10 (2018), 4398--4405. arXiv:https://doi.org/10.1021/acs.molpharmaceut.8b00839 30180591.

[11]

Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev. 2019. MolecularRNN: Generating realistic molecular graphs with optimized properties. arXiv preprint arXiv:1905.13372 (2019).

[12]

Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Günter Klambauer. 2018. Fréchet ChemblNet Distance: A metric for generative models for molecules. CoRR abs/1803.09518 (2018). arXiv:1803.09518 http://arxiv.org/abs/1803.09518

[13]

Jean-Louis Reymond, Lars Ruddigkeit, Lorenz Blum, and Ruud van Deursen. 2012. The enumeration of chemical space. Wiley Interdisciplinary Reviews: Computational Molecular Science 2, 5 (2012), 717--733. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1104

[14]

Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller. 2018. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Central Science 4, 1 (2018), 120--131. arXiv:https://doi.org/10.1021/acscentsci.7b00512 29392184.

[15]

David Weininger. 1988. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences 28, 1 (1988), 31--36. arXiv:https://pubs.acs.org/doi/pdf/10.1021/ci00057a005

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Cited By

Jiang JKe LChen LDou BZhu YLiu JZhang BZhou TWei G(2024)Transformer technology in molecular scienceWIREs Computational Molecular Science10.1002/wcms.172514:4Online publication date: 4-Aug-2024
https://doi.org/10.1002/wcms.1725
Tian YYang ZWang HYan A(2023) Prediction of bioactivities of microsomal prostaglandin E 2 synthase‐1 inhibitors by machine learning algorithms Chemical Biology & Drug Design10.1111/cbdd.14214101:6(1307-1321)Online publication date: 20-Feb-2023
https://doi.org/10.1111/cbdd.14214
Monteiro NPereira TMachado AOliveira JAbbasi MArrais J(2023)FSM-DDTRComputers in Biology and Medicine10.1016/j.compbiomed.2023.107285164:COnline publication date: 18-Oct-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107285
Show More Cited By

Index Terms

De novo drug design using self attention mechanism
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
  2. Physical sciences and engineering
    1. Chemistry
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation

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cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 March 2020

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SAC '20

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SIGAPP

SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Jiang JKe LChen LDou BZhu YLiu JZhang BZhou TWei G(2024)Transformer technology in molecular scienceWIREs Computational Molecular Science10.1002/wcms.172514:4Online publication date: 4-Aug-2024
https://doi.org/10.1002/wcms.1725
Tian YYang ZWang HYan A(2023) Prediction of bioactivities of microsomal prostaglandin E 2 synthase‐1 inhibitors by machine learning algorithms Chemical Biology & Drug Design10.1111/cbdd.14214101:6(1307-1321)Online publication date: 20-Feb-2023
https://doi.org/10.1111/cbdd.14214
Monteiro NPereira TMachado AOliveira JAbbasi MArrais J(2023)FSM-DDTRComputers in Biology and Medicine10.1016/j.compbiomed.2023.107285164:COnline publication date: 18-Oct-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107285
Tong XLiu XTan XLi XJiang JXiong ZXu TJiang HQiao NZheng M(2021)Generative Models for De Novo Drug DesignJournal of Medicinal Chemistry10.1021/acs.jmedchem.1c0092764:19(14011-14027)Online publication date: 17-Sep-2021
https://doi.org/10.1021/acs.jmedchem.1c00927
Wang HQin ZYan A(2021)Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithmsMolecular Diversity10.1007/s11030-020-10165-425:3(1597-1616)Online publication date: 3-Feb-2021
https://doi.org/10.1007/s11030-020-10165-4

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