Authors - Manav Thakar, Nischay Agrawal, Jaykumar Gandharva, Manish Singh Abstract - Predicting and understanding the inhibitory activity associated with Breast Cancer resistance protein can assist in the drug discovery process by anticipating the potential drug resistance and drug-drug interactions. Prediction of BCRP inhibitors using machine learning can accelerate the identification of BCRP inhibitors by analyzing large datasets, finding patterns in molecular structures, and predicting interactions that would be time-consuming and expensive through traditional methods like high-throughput screening or trial-and-error experimentation. In the literature, machine learning has been employed to develop techniques for predicting BCRP inhibition. However, these methods often exhibit low prediction accuracy, highlighting the need for improved prediction techniques with enhanced accuracy. In this research, BCRP inhibition prediction has been carried out using features spaces fusion to enhance the features information with richer representation of data incorporating complementary aspects of molecule to get the increased accuracy for discovery of inhibitors for drugs of breast cancer. The experimental results show that the proposed technique has increased accuracy and precision for the discovery of BCRP inhibitors. The accuracy of the proposed technique is 97% which is higher than the techniques developed in literature. The study demonstrates that enhancing the features information by combining various compound properties creates a more richer and comprehensive feature space. This enhanced feature representation can significantly help in identifying BCRP inhibitors specifically and contribute to advancements in drug discovery overall.
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