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. 2020 Jul 14;11(8):1496-1505.
doi: 10.1021/acsmedchemlett.0c00088. eCollection 2020 Aug 13.

The Advent of Generative Chemistry

Quentin Vanhaelen  1 , Yen-Chu Lin  1   2 , Alex Zhavoronkov  1
Affiliations

The Advent of Generative Chemistry

Quentin Vanhaelen et al. ACS Med Chem Lett. .

Abstract

Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.

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Conflict of interest statement

The authors declare the following competing financial interest(s): The authors are affiliated with Insilico Medicine, a company developing an AI-based end-to-end integrated pipeline for target identification and drug discovery and engaged in aging and cancer research.

Figures

Figure 1
Figure 1
Timeline summarizing the development of ML, DL, and the learning concepts including GAN and RL. Those technologies were critical for the emergence of generative chemistry.
Figure 2
Figure 2
Schematic representation of architectures used in generative chemistry. The VAE/AAE model (top) and the GAN-RL model (bottom) have been successful in generating molecular structures of compounds with desired sets of properties.
Figure 3
Figure 3
Different approaches suggested for representing molecular structures. Although the first published DL-based model for de novo drug design used fingerprints to represent the molecules, SMILES is currently the most used format for encoding molecular structures. Other representations such as graphs are also attracting interest. Combining different encoding formats allow building a more detailed description of the molecular structures leading to better performance of de novo design methods.
See this image and copyright information in PMC

References

    1. Mamoshina P.; et al. Applications of Deep Learning in Biomedicine. Mol. Pharmaceutics 2016, 13 (5), 1445–54. 10.1021/acs.molpharmaceut.5b00982. - DOI - PubMed
    1. Zhavoronkov A.; et al. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res. Rev. 2019, 49, 49–66. 10.1016/j.arr.2018.11.003. - DOI - PubMed
    1. Goodfellow I.NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv (Machine Learning), April 3, 2017, 1701.00160, ver. 4. https://arxiv.org/abs/1701.00160v4.
    1. Goodfellow I. J.et al. Generative Adversarial Networks. arXiv (Machine Learning), June 10, 2014, 1406.2661, ver. 1. https://arxiv.org/abs/1406.2661.
    1. Schneider G.; Clark D. E. Automated De Novo Drug Design - “Are we nearly there yet?. Angew. Chem., Int. Ed. 2019, 58 (32), 10792–10803. 10.1002/anie.201814681. - DOI - PubMed