Towards Drug Discovery: A Comparative Study of Machine Learning-enhanced QSAR Prediction
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85191656880
Journal Title
KST 2024 - 16th International Conference on Knowledge and Smart Technology
Start Page
79
End Page
84
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SCOPUS
Bibliographic Citation
KST 2024 - 16th International Conference on Knowledge and Smart Technology (2024) , 79-84
Suggested Citation
Phimonjit S., Thankam S., Techahongsa P., Thaipisutikul T. Towards Drug Discovery: A Comparative Study of Machine Learning-enhanced QSAR Prediction. KST 2024 - 16th International Conference on Knowledge and Smart Technology (2024) , 79-84. 84. doi:10.1109/KST61284.2024.10499658 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98238
Title
Towards Drug Discovery: A Comparative Study of Machine Learning-enhanced QSAR Prediction
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Author's Affiliation
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Abstract
In the field of drug discovery, the accurate prediction of bioactive molecules' interactions with biological targets is a significant challenge, limited by the predictive accuracy and handling of complex data in traditional Quantitative Structure-Activity Relationship (QSAR) models. Therefore, our study introduces an innovative approach that integrates advanced machine learning (ML) techniques with QSAR modeling, offering a solution to these limitations. We conducted a comprehensive comparative analysis of various ML algorithms, including decision trees, random forests, support vector machines, deep learning, and ensemble methods, assessing their effectiveness in enhancing QSAR predictions. Our results demonstrate notable improvements in predictive accuracy and efficiency, highlighting the potential of ML-enhanced QSAR models especially with tree-based models in drug discovery. This study contributes significantly to the field by providing a detailed comparison of ML algorithms for QSAR modeling and paving the way for more efficient and accurate drug discovery processes.