INHIBPRED: A WEB SERVER FOR PREDICTING INHIBITORY ACTIVITY OF MOLECULES AGAINST HUMAN HDAC6 PROTEIN

Oral squamous cell carcinoma (OSCC) is the most commonly occurring malignancy of the oral cavity. Over the years, the incidence and morbidity rate of Oral Cancer patients has increased worldwide. In the last few decades, although oral cancer treatment modalities have advanced, the survival rate of oral cancer patients is very low. Thus, there arises a need for the identification of new drug targets besides the development of new and effective drugs for this disease. This paper describes the development of Quantitative Structure-Activity Relationship (QSAR) models using machine learning techniques such as Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) for predicting the inhibitory activity (IC₅₀) of anti-cancer compounds against HDAC6 protein. After data pre-processing and selection of relevant features, predictive models were developed, and the top two best performing models were selected based on the parameters such as Squared Correlation Coefficient (r²) and Mean Absolute Error (MAE). Moreover, based on this study, an open-source platform (www.inhibpred.com) has been developed, thus facilitating the identification of promising leads against this disease. Therefore, this free online resource will enable the drug discovery community to evaluate and know the potential of their compound library prior to actual synthesis and experimental testing, thus saving a lot of time, labour, and huge expenses