Yasmine Eid Mahmoud YousefAyman El-KilanyAyman El-KilanyYassin M. NissanEhab E. Hassanein2024-11-092024-11-092024-12Yousef, Y. E. M., El-Kilany, A., Ali, F., Nissan, Y. M., & Hassanein, E. E. (2024). Deep Learning-Assisted Compound Bioactivity Estimation Framework. Egyptian Informatics Journal, 28, 100558. https://doi.org/10.1016/j.eij.2024.100558https://repository.msa.edu.eg/handle/123456789/6197Drug Discovery is a highly complicated process. On average, it takes six to twelve years to manufacture a new drug and have the product released in the market. It is of utmost importance to find methods that would accelerate the manufacturing process. This significant challenge in drug development can be addressed using deep learning techniques. The aim of this paper is to propose a deep learning-based framework that can help chemists examine compound biological activity in a more accurate manner. The proposed framework employs autoencoder for data representation of the compounds data, which is then classified using deep neural network followed by building a customized deep regression model to estimate an accurate value of the compound bioactivity. The proposed framework achieved an accuracy of 89% in autoencoder reconstruction error, 79.01% in classification, and MAE of 2.4 while predicting compound bioactivity using deep regression model.enDrug discoveryDeep learningRegressionAuto-encoderClassificationArticlehttps://doi.org/10.1016/j.eij.2024.100558