An applied computational intelligence project to determine whether LightGBM is a suitable replacement for the Random Forest, used in QSAR models.
The project was divided into three parts, because three different applications were tested. All scripts can be run directly in google colab environment, links to the codes and in the respective environment can be found right here.
- Genotoxicity_Classifier
- MAO-B_Regressor
- CCR5_Regressor
Stawiski, M., Meier, P., Dornberger, R., Hanne, T. (2023). Using the Light Gradient Boosting Machine for Prediction in QSAR Models. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1435-7_10
