Deep Learning Based Drug Target Binding Prediction

Authors: Priyanka Pawar, Omkar Wakchaure, Vaibhav Waware, Suhana Patel, Dr. A. V. Markad

Country: India

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Abstract: Accurately predicting the binding affinity between drugs and their target proteins is crucial for drug discovery and development. This project develops a machine learning-based model utilizing Random Forest (RF) and Support Vector Machine (SVM) to predict drug-target interactions. The model leverages structural and sequence-based features of proteins along with atomic and bond-level representations of drugs. Trained on a large dataset of known interactions, the model employs advanced feature engineering and optimization techniques to enhance accuracy. Performance evaluation shows that the proposed approach, CGraphDTA, outperforms existing methods, providing a reliable and efficient solution for drug-target interaction prediction. This work contributes to accelerating drug discovery while reducing costs and computational complexity.

Keywords: Deep Learning, Drug-Target Binding Prediction, Binding Affinity, Drug Discovery, Protein Structure, Drug Structure, Random Forest, Support Vector Machine

Paper Id: 232390

Published On: 2025-04-18

Published In: Volume 13, Issue 2, March-April 2025

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