Toxicity prediction via algebraic graph-assisted bidirectional transformers
Drug lead optimization is a crucial stage in drug development that seeks to improve the efficacy and safety of potential drug candidates. However, toxicity is a major concern that can hinder the development process of drugs, especially when it is harmful to human bodies. In this paper, we present Al...
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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2023
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| 在線閱讀: | https://hdl.handle.net/10356/166443 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Drug lead optimization is a crucial stage in drug development that seeks to improve the efficacy and safety of potential drug candidates. However, toxicity is a major concern that can hinder the development process of drugs, especially when it is harmful to human bodies. In this paper, we present Algebraic Graph-assisted Bidirectional Transformers (AGBT), a novel approach to improve drug lead optimization by enhancing toxicity predictions and identifying its adverse effects on humans. This approach utilizes machine learning algorithms to predict the toxicity of potential drug candidates based on their chemical structures and properties. The effectiveness of the AGBT approach will be evaluated against 4 datasets and its results demonstrate that AGBT can outperform current works in certain situations. To improve the discussed AGBT method in this paper, further works have been suggested to pave the way for safer drug development in the future. |
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