ReGen-DTI : A novel generative drug target interaction model for predicting potential drug candidates against SARS-COV2
Copyright © 2023 Elsevier Ltd. All rights reserved..
Covid-19 has caused massive numbers of infections and fatalities globally. In response, there has been a large-scale experimental and computational research effort to study and develop drugs. Towards this, Deep learning techniques are used for the generation of potential novel drug candidates that are proven to be effective against exploring large molecular search spaces. Recent advances in reinforcement learning in conjunction with generative techniques has proven to be a promising field in the area of drug discovery. In this regard, we propose a generative drug discovery approach using reinforcement techniques for sampling novel molecules that bind to the main protease of SARS-COV2. The generative method reported significant validity scores for the generated novel molecules and captured the underlying features of the training molecules. Further, the model is fine-tuned on existing re-purposed molecules which are active towards specific target proteins based on similarity metrics. Upon fine tuning the model generated 92.71% valid, 93.55% unique, and 100% novel molecules. Unlike previous methods which are dependent on docking procedures, we proposed a deep learning based novel drug target interaction (DTI) model to find the binding affinity between candidate molecules and target protease sequence. Finally, the binding affinity of the generated molecules is predicted against the 3CLPro main protease by using the proposed DTI model. Most of the generated molecules have shown binding affinity scores <100 nM (lower the better), which are significantly better compared to the existing commercial drugs including Remdesevir.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:106 |
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| Enthalten in: |
Computational biology and chemistry - 106(2023) vom: 25. Okt., Seite 107927 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sivangi, Kaushik Bhargav [VerfasserIn] |
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| Links: |
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| Themen: |
Covid-19 |
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| Anmerkungen: |
Date Completed 06.09.2023 Date Revised 06.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiolchem.2023.107927 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM359994113 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Covid-19 has caused massive numbers of infections and fatalities globally. In response, there has been a large-scale experimental and computational research effort to study and develop drugs. Towards this, Deep learning techniques are used for the generation of potential novel drug candidates that are proven to be effective against exploring large molecular search spaces. Recent advances in reinforcement learning in conjunction with generative techniques has proven to be a promising field in the area of drug discovery. In this regard, we propose a generative drug discovery approach using reinforcement techniques for sampling novel molecules that bind to the main protease of SARS-COV2. The generative method reported significant validity scores for the generated novel molecules and captured the underlying features of the training molecules. Further, the model is fine-tuned on existing re-purposed molecules which are active towards specific target proteins based on similarity metrics. Upon fine tuning the model generated 92.71% valid, 93.55% unique, and 100% novel molecules. Unlike previous methods which are dependent on docking procedures, we proposed a deep learning based novel drug target interaction (DTI) model to find the binding affinity between candidate molecules and target protease sequence. Finally, the binding affinity of the generated molecules is predicted against the 3CLPro main protease by using the proposed DTI model. Most of the generated molecules have shown binding affinity scores <100 nM (lower the better), which are significantly better compared to the existing commercial drugs including Remdesevir | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Covid-19 | |
| 650 | 4 | |a Deep neural networks | |
| 650 | 4 | |a Drug discovery | |
| 650 | 4 | |a Drug target interaction | |
| 650 | 4 | |a Generative models | |
| 650 | 4 | |a Reinforcement learning | |
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| 700 | 1 | |a Amilpur, Santhosh |e verfasserin |4 aut | |
| 700 | 1 | |a Dasari, Chandra Mohan |e verfasserin |4 aut | |
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