Comparative Studies On Drug-Target Interaction Prediction Using Machine Learning and Deep Learning Methods With Different Molecular Descriptors
Haswani Ismail, Nurul Hashimah Ahamed Hassain Malim, Siti Zuraidah Mohamad Zobir, Habibah A. Wahab · 2021
New drugs introduced in the market should not only be able to cure the targeted indications, but also should have no or minimal side effects to our body. Traditional drug discovery including in-vitro and in-vivo methods is a challenging, costly and time-consuming process. Thus, in-silico method was introduced where the Drug-Target Interaction (DTI) is predicted using computation methods. A reliable DTI prediction model would save a lot of time and costs for drug discovery since not all drug candidates but only the actives candidates would be further tested in the wet labs. In this paper, various binary classifier models have been developed to predict the activeness or inactiveness of compounds (drugs) from ChEMBL database against 11 activity classes (targets). The prediction methods used in this study are Support Vector Machine (SVM), Naive Bayes (NB), Feed-forward Sequential Model (SEQ) and Convolutional Neural Networks (CNN). The molecular descriptors used to convert the SMILES chemical notation into binary fingerprint are Extended Connectivity Fingerprints 4 (ECFP4) and Maccs Key (MACCS). The results shows that the 11 predictive models have potential to be applied in real-world situation to predict the interaction of compounds against 11 target proteins as it is found that most machine learning and deep learning methods works better with data represented in ECFP4 compared to MACCS molecular descriptor.