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Research Article
A Merged Molecular Representation Learning for Molecular Properties Prediction with a Web-based Service
Jongsuk Lee
Korea Institute of Science & Technology Information
Corresponding Author
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Deep learning has brought a dramatic development in molecular property prediction which is crucial in the field of drug discovery. Various methods such as fingerprint, SMILES, graphs have been proposed for representing molecules. Recently, unlabeled molecule data is used to improve performance for various pre-training methods. The main challenge of molecular properties predictions is designing a data representation and model that can show good performance for various datasets. However, performance deviation due to scarcity of dataset exists in constructing the model. We propose a new self-supervised method to learn the characteristics and structures of molecules by integrating existing methods. The key of our model is learning structures with matrix embedding and learning logics that can infer descriptors via QED prediction. As a result, our method improves the generalization of the data and achieves the best average performance by benchmarking downstream tasks. Moreover, we develop a web-based fine-tuning service to utilize our model on various tasks.
Keywords
dramatic development, molecular property, drug discovery, fingerprint, SMILES, graphs
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On 15 Jan, 2021
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This preprint is under consideration at Scientific Reports. 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
A Merged Molecular Representation Learning for Molecular Properties Prediction with a Web-based Service
Jongsuk Lee
Korea Institute of Science & Technology Information
Corresponding Author
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 21 Dec, 2020
No community comments so far
Reviewer #1 agreed
On 15 Jan, 2021
Reviewers invited
Invitations sent on 12 Jan, 2021
Editor assigned
On 11 Jan, 2021
Editor invited
On 18 Dec, 2020
Submission checks complete
On 18 Dec, 2020
First submitted
On 16 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Deep learning has brought a dramatic development in molecular property prediction which is crucial in the field of drug discovery. Various methods such as fingerprint, SMILES, graphs have been proposed for representing molecules. Recently, unlabeled molecule data is used to improve performance for various pre-training methods. The main challenge of molecular properties predictions is designing a data representation and model that can show good performance for various datasets. However, performance deviation due to scarcity of dataset exists in constructing the model. We propose a new self-supervised method to learn the characteristics and structures of molecules by integrating existing methods. The key of our model is learning structures with matrix embedding and learning logics that can infer descriptors via QED prediction. As a result, our method improves the generalization of the data and achieves the best average performance by benchmarking downstream tasks. Moreover, we develop a web-based fine-tuning service to utilize our model on various tasks.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.