Predictive Modeling and Drug Repurposing for Type-II Diabetes.

ABSTRACT: Diabetes mellitus (DM) is a global health concern, and dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target. The study used three machine learning and deep learning models to predict potential DPP-4 inhibitors using a curated data set of 6,750 compounds. The models included support vector machine (SVM), random forest (RF), naive Bayes (NB), and multitask deep neural network (MTDNN). The MTDNN model demonstrated strong predictive performance, achieving 98.62% train accuracy and 98.42% test accuracy for predicting DPP-4 inhibitors and a correlation coefficient of 0.979 for training and 0.977 for the test data set, with low training and test errors while predicting corresponding IC₅₀ values. The MTDNN model predicted potential inhibitors using an external data set of FDA-appr

SUBMITTER: Wankhade N 

PROVIDER: S-EPMC11571088 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature