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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166443 |
| _version_ | 1824454911424724992 |
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| author | Ooi, Yen Sun |
| author2 | Xia Kelin |
| author_facet | Xia Kelin Ooi, Yen Sun |
| author_sort | Ooi, Yen Sun |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:29:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166443 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:29:50Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1664432023-05-01T15:36:20Z Toxicity prediction via algebraic graph-assisted bidirectional transformers Ooi, Yen Sun Xia Kelin School of Physical and Mathematical Sciences xiakelin@ntu.edu.sg Science::Mathematics::Discrete mathematics::Graph theory 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. Bachelor of Science in Mathematical Sciences 2023-04-28T07:52:42Z 2023-04-28T07:52:42Z 2023 Final Year Project (FYP) Ooi, Y. S. (2023). Toxicity prediction via algebraic graph-assisted bidirectional transformers. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166443 https://hdl.handle.net/10356/166443 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics::Discrete mathematics::Graph theory Ooi, Yen Sun Toxicity prediction via algebraic graph-assisted bidirectional transformers |
| title | Toxicity prediction via algebraic graph-assisted bidirectional transformers |
| title_full | Toxicity prediction via algebraic graph-assisted bidirectional transformers |
| title_fullStr | Toxicity prediction via algebraic graph-assisted bidirectional transformers |
| title_full_unstemmed | Toxicity prediction via algebraic graph-assisted bidirectional transformers |
| title_short | Toxicity prediction via algebraic graph-assisted bidirectional transformers |
| title_sort | toxicity prediction via algebraic graph assisted bidirectional transformers |
| topic | Science::Mathematics::Discrete mathematics::Graph theory |
| url | https://hdl.handle.net/10356/166443 |
| work_keys_str_mv | AT ooiyensun toxicitypredictionviaalgebraicgraphassistedbidirectionaltransformers |