View Proposal

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Proposer

Heba Elshimy

Title

Predicting Drug-Drug Interactions for Safe Prescriptions: a Deep Learning Approach

Goal

Description

Drug-drug interactions occur when two or more drugs interact with each other. These could result in a range of outcomes from reducing the efficacy of one or both drugs to dangerous side effects such as increased blood pressure or drowsiness. Impact: Increasing co-morbidities with age often results in the prescription of multiple drugs simultaneously. Predicting possible drug-drug interactions before they are prescribed is thus an important step in preventing these adverse outcomes.

Resources

Datasets: https://tdcommons.ai/multi_pred_tasks/ddi/ Tools: RDIKit for molecular structures and fingerprinting: https://www.rdkit.org/docs/index.html Relevant research: 1. An accurate prediction of drug–drug interactions and side effects by using integrated convolutional and BiLSTM networks: https://doi.org/10.1016/j.chemolab.2024.105304 2. KITE-DDI: A Knowledge Graph Integrated Transformer Model for Accurately Predicting Drug-Drug Interaction Events From Drug SMILES and Biomedical Knowledge Graph: DOI: 10.1109/ACCESS.2025.3547594 3. A comprehensive review of deep learning-based approaches for drug-drug interaction prediction: https://doi.org/10.1093/bfgp/elae052 4. Attention is all you need: utilizing attention in AI-enabled drug discovery: https://doi.org/10.1093/bib/bbad467

Background

Python, Pytorch/Tensorflow/Keras

Url

Difficulty Level

High

Ethical Approval

None

Number Of Students

1

Supervisor

Heba Elshimy

Keywords

deep learning, drug interactions

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Science in Artificial Intelligence
Master of Science in Data Science
Bachelor of Science in Computing Science