TY - GEN
T1 - Hierarchical Modeling of Binding Affinity Prediction Using Machine LearningTechniques
AU - D'Souza, Sofia
AU - Prema, K. V.
AU - Balaji, S.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Predicting the binding affinity of compounds is an essential task in drug discovery. In silico QSAR regression and classification models to predict drug-Target interaction can help speed up identifying the most potent compounds. Machine learning-based QSAR models were developed to predict the binding affinity of compounds against different targets using the experimental values or labels. In this work, we modeled the binding affinity prediction of SARS-3CL protease inhibitors using hierarchical modeling. We developed the Base classification and regression models using KNN, SVM, RF, and XGBoost techniques. Further, the predictions of the base models were concatenated and provided as inputs for the stacked models. The results indicate that stacking of models hierarchically leads to improved performances on both classification and regression endpoints.
AB - Predicting the binding affinity of compounds is an essential task in drug discovery. In silico QSAR regression and classification models to predict drug-Target interaction can help speed up identifying the most potent compounds. Machine learning-based QSAR models were developed to predict the binding affinity of compounds against different targets using the experimental values or labels. In this work, we modeled the binding affinity prediction of SARS-3CL protease inhibitors using hierarchical modeling. We developed the Base classification and regression models using KNN, SVM, RF, and XGBoost techniques. Further, the predictions of the base models were concatenated and provided as inputs for the stacked models. The results indicate that stacking of models hierarchically leads to improved performances on both classification and regression endpoints.
UR - http://www.scopus.com/inward/record.url?scp=85124803521&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124803521&partnerID=8YFLogxK
U2 - 10.1109/DISCOVER52564.2021.9663690
DO - 10.1109/DISCOVER52564.2021.9663690
M3 - Conference contribution
AN - SCOPUS:85124803521
T3 - 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2021 - Proceedings
SP - 61
EP - 65
BT - 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2021
Y2 - 19 November 2021 through 20 November 2021
ER -