CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention
Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affini...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/23/15/8453 |
| _version_ | 1797441923887136768 |
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| author | Ashutosh Ghimire Hilal Tayara Zhenyu Xuan Kil To Chong |
| author_facet | Ashutosh Ghimire Hilal Tayara Zhenyu Xuan Kil To Chong |
| author_sort | Ashutosh Ghimire |
| collection | DOAJ |
| description | Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities. |
| first_indexed | 2024-03-09T12:33:15Z |
| format | Article |
| id | doaj.art-c94542dad8ed4dd687c03d7cdc1dfb74 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T12:33:15Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-c94542dad8ed4dd687c03d7cdc1dfb742023-11-30T22:28:18ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-07-012315845310.3390/ijms23158453CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-AttentionAshutosh Ghimire0Hilal Tayara1Zhenyu Xuan2Kil To Chong3Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaSchool of International Engineering and Science, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Biological Sciences, The University of Texas at Dallas, Richardson, TX 75080, USADepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaDrug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.https://www.mdpi.com/1422-0067/23/15/8453drug–target interactionbinding affinityattentionconvolution neural networkdeep learningartificial intelligence |
| spellingShingle | Ashutosh Ghimire Hilal Tayara Zhenyu Xuan Kil To Chong CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention International Journal of Molecular Sciences drug–target interaction binding affinity attention convolution neural network deep learning artificial intelligence |
| title | CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention |
| title_full | CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention |
| title_fullStr | CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention |
| title_full_unstemmed | CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention |
| title_short | CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention |
| title_sort | csatdta prediction of drug target binding affinity using convolution model with self attention |
| topic | drug–target interaction binding affinity attention convolution neural network deep learning artificial intelligence |
| url | https://www.mdpi.com/1422-0067/23/15/8453 |
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