Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks
Cancer is one of the leading causes of deaths and one of the treatments of cancer cure is the use of drugs that inhibits the growth of cancer tumors. Hance, it is essential to be able to predict the response of cancer drugs to assess the effectiveness of each drug in slowing down the growth of abnor...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/156576 |
| _version_ | 1811681210652426240 |
|---|---|
| author | Asok Kumar Gaurav |
| author2 | Jagath C Rajapakse |
| author_facet | Jagath C Rajapakse Asok Kumar Gaurav |
| author_sort | Asok Kumar Gaurav |
| collection | NTU |
| description | Cancer is one of the leading causes of deaths and one of the treatments of cancer cure is the use of drugs that inhibits the growth of cancer tumors. Hance, it is essential to be able to predict the response of cancer drugs to assess the effectiveness of each drug in slowing down the growth of abnormal malignant cancer cells. Using computational models to predicting the response of each cancer drug can improve the chances of the patient’s successful recovery
Currently, there is no ideal cancer drug response prediction method. Hence, in this report we are predicting the drug responses using drug the Extended-Connectivity Fingerprints (ECFPs) of cancer drugs as input to a graph neural network. Extended-Connectivity Fingerprints (ECFPs) of a drug captures the substructural information the drug, and hence gives a numerical representation any drug from its molecular formula.
In previous research, to model the molecular graphs with GNN, certain atom features, such as atom symbol, atom degree, etc., of drugs were applied to form the node features of GNN. In this project, Extended-Connectivity Fingerprints (ECFPs) are also extracted to extend the node features. We demonstrate that GNN using ECFPs features of drugs are better in representing drug chemical features than using simple atom features alone.
In this project, three types of GNN are implemented for prediction of drug responses from chemical features: Graph Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks (GAT). |
| first_indexed | 2024-10-01T03:37:20Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156576 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:37:20Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1565762022-04-20T07:48:42Z Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks Asok Kumar Gaurav Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering Cancer is one of the leading causes of deaths and one of the treatments of cancer cure is the use of drugs that inhibits the growth of cancer tumors. Hance, it is essential to be able to predict the response of cancer drugs to assess the effectiveness of each drug in slowing down the growth of abnormal malignant cancer cells. Using computational models to predicting the response of each cancer drug can improve the chances of the patient’s successful recovery Currently, there is no ideal cancer drug response prediction method. Hence, in this report we are predicting the drug responses using drug the Extended-Connectivity Fingerprints (ECFPs) of cancer drugs as input to a graph neural network. Extended-Connectivity Fingerprints (ECFPs) of a drug captures the substructural information the drug, and hence gives a numerical representation any drug from its molecular formula. In previous research, to model the molecular graphs with GNN, certain atom features, such as atom symbol, atom degree, etc., of drugs were applied to form the node features of GNN. In this project, Extended-Connectivity Fingerprints (ECFPs) are also extracted to extend the node features. We demonstrate that GNN using ECFPs features of drugs are better in representing drug chemical features than using simple atom features alone. In this project, three types of GNN are implemented for prediction of drug responses from chemical features: Graph Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks (GAT). Bachelor of Engineering (Computer Science) 2022-04-20T07:48:42Z 2022-04-20T07:48:42Z 2022 Final Year Project (FYP) Asok Kumar Gaurav (2022). Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156576 https://hdl.handle.net/10356/156576 en SCSE21-0209 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Asok Kumar Gaurav Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks |
| title | Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks |
| title_full | Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks |
| title_fullStr | Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks |
| title_full_unstemmed | Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks |
| title_short | Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks |
| title_sort | predicting drug responses from extended connectivity fingerprints ecfps of drugs by using graph neural networks |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/156576 |
| work_keys_str_mv | AT asokkumargaurav predictingdrugresponsesfromextendedconnectivityfingerprintsecfpsofdrugsbyusinggraphneuralnetworks |