Deep Learning-Assisted Compound Bioactivity Estimation Framework
Drug Discovery is a highly complicated process. On average, it takes six to twelve years to manufacture a new drug and have the product released in the market. It is of utmost importance to find methods that would accelerate the manufacturing process. This significant challenge in drug development c...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S111086652400121X |
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| author | Yasmine Eid Mahmoud Yousef Ayman El-Kilany Farid Ali Yassin M. Nissan Ehab E. Hassanein |
| author_facet | Yasmine Eid Mahmoud Yousef Ayman El-Kilany Farid Ali Yassin M. Nissan Ehab E. Hassanein |
| author_sort | Yasmine Eid Mahmoud Yousef |
| collection | DOAJ |
| description | Drug Discovery is a highly complicated process. On average, it takes six to twelve years to manufacture a new drug and have the product released in the market. It is of utmost importance to find methods that would accelerate the manufacturing process. This significant challenge in drug development can be addressed using deep learning techniques. The aim of this paper is to propose a deep learning-based framework that can help chemists examine compound biological activity in a more accurate manner. The proposed framework employs autoencoder for data representation of the compounds data, which is then classified using deep neural network followed by building a customized deep regression model to estimate an accurate value of the compound bioactivity. The proposed framework achieved an accuracy of 89% in autoencoder reconstruction error, 79.01% in classification, and MAE of 2.4 while predicting compound bioactivity using deep regression model. |
| format | Article |
| id | doaj-art-c0dd1a4db37b4bbe8bc98b80c4b7c75c |
| institution | OA Journals |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-c0dd1a4db37b4bbe8bc98b80c4b7c75c2025-08-20T01:56:20ZengElsevierEgyptian Informatics Journal1110-86652024-12-012810055810.1016/j.eij.2024.100558Deep Learning-Assisted Compound Bioactivity Estimation FrameworkYasmine Eid Mahmoud Yousef0Ayman El-Kilany1Farid Ali2Yassin M. Nissan3Ehab E. Hassanein4Faculty of Computer Science, October University for Modern Sciences and Arts, Cairo, Egypt; Corresponding author.Information Systems Department, Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, EgyptInformation Technology Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, EgyptPharmaceutical chemistry department faculty of pharmacy Cairo University, Cairo, Egypt; Pharmaceutical chemistry department faculty of pharmacy MSA University, Cairo, EgyptInformation Systems Department, Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, EgyptDrug Discovery is a highly complicated process. On average, it takes six to twelve years to manufacture a new drug and have the product released in the market. It is of utmost importance to find methods that would accelerate the manufacturing process. This significant challenge in drug development can be addressed using deep learning techniques. The aim of this paper is to propose a deep learning-based framework that can help chemists examine compound biological activity in a more accurate manner. The proposed framework employs autoencoder for data representation of the compounds data, which is then classified using deep neural network followed by building a customized deep regression model to estimate an accurate value of the compound bioactivity. The proposed framework achieved an accuracy of 89% in autoencoder reconstruction error, 79.01% in classification, and MAE of 2.4 while predicting compound bioactivity using deep regression model.http://www.sciencedirect.com/science/article/pii/S111086652400121XDrug discoveryDeep learningRegressionAuto-encoderClassification |
| spellingShingle | Yasmine Eid Mahmoud Yousef Ayman El-Kilany Farid Ali Yassin M. Nissan Ehab E. Hassanein Deep Learning-Assisted Compound Bioactivity Estimation Framework Egyptian Informatics Journal Drug discovery Deep learning Regression Auto-encoder Classification |
| title | Deep Learning-Assisted Compound Bioactivity Estimation Framework |
| title_full | Deep Learning-Assisted Compound Bioactivity Estimation Framework |
| title_fullStr | Deep Learning-Assisted Compound Bioactivity Estimation Framework |
| title_full_unstemmed | Deep Learning-Assisted Compound Bioactivity Estimation Framework |
| title_short | Deep Learning-Assisted Compound Bioactivity Estimation Framework |
| title_sort | deep learning assisted compound bioactivity estimation framework |
| topic | Drug discovery Deep learning Regression Auto-encoder Classification |
| url | http://www.sciencedirect.com/science/article/pii/S111086652400121X |
| work_keys_str_mv | AT yasmineeidmahmoudyousef deeplearningassistedcompoundbioactivityestimationframework AT aymanelkilany deeplearningassistedcompoundbioactivityestimationframework AT faridali deeplearningassistedcompoundbioactivityestimationframework AT yassinmnissan deeplearningassistedcompoundbioactivityestimationframework AT ehabehassanein deeplearningassistedcompoundbioactivityestimationframework |