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Research article
Diversity Oriented Deep Reinforcement Learning for Targeted Molecule Generation
Maryam Abbasi
University of Coimbra Centre for Informatics and Systems: Universidade de Coimbra Centro de Informatica e Sistemas
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9011-0734
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties.
The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far.
To demonstrate the effectiveness of the method, the Generator is trained to design molecules with high inhibitory power for the adenosine A2A and κ opioid receptors. The results reveal that the model can effectively modify the biological affinity of the newly generated molecules towards the craved direction. More importantly, it was possible to find auspicious sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.
Keywords
Drug Design, SMILES, Reinforcement Learning, RNN
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CURRENT STATUS: Under Review
Version 1
Posted 24 Nov, 2020
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Editorial decision: Major revision
On 09 Jan, 2021
Review #1 received
Received 08 Jan, 2021
Review #2 received
Received 27 Dec, 2020
Reviewer #2 agreed
On 14 Dec, 2020
Reviewers invited
Invitations sent on 13 Dec, 2020
Reviewer #1 agreed
On 13 Dec, 2020
Editor assigned
On 17 Nov, 2020
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On 17 Nov, 2020
Editor invited
On 17 Nov, 2020
First submitted
On 12 Nov, 2020
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This preprint is under consideration at Journal of Cheminformatics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Diversity Oriented Deep Reinforcement Learning for Targeted Molecule Generation
Maryam Abbasi
University of Coimbra Centre for Informatics and Systems: Universidade de Coimbra Centro de Informatica e Sistemas
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9011-0734
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 24 Nov, 2020
No community comments so far
Editorial decision: Major revision
On 09 Jan, 2021
Review #1 received
Received 08 Jan, 2021
Review #2 received
Received 27 Dec, 2020
Reviewer #2 agreed
On 14 Dec, 2020
Reviewers invited
Invitations sent on 13 Dec, 2020
Reviewer #1 agreed
On 13 Dec, 2020
Editor assigned
On 17 Nov, 2020
Submission checks complete
On 17 Nov, 2020
Editor invited
On 17 Nov, 2020
First submitted
On 12 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties.
The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far.
To demonstrate the effectiveness of the method, the Generator is trained to design molecules with high inhibitory power for the adenosine A2A and κ opioid receptors. The results reveal that the model can effectively modify the biological affinity of the newly generated molecules towards the craved direction. More importantly, it was possible to find auspicious sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.