Artificial intelligence paradigm for ligand-based virtual screening on the drug discovery of type 2 diabetes mellitus

Abstract

Background New dipeptidyl peptidase-4 (DPP-4) inhibitors need to be developed to be used as agents with low adverse effects for the treatment of type 2 diabetes mellitus. This study aims to build quantitative structure-activity relationship (QSAR) models using the artificial intelligence paradigm. Rotation Forest and Deep Neural Network (DNN) are used to predict QSAR models. We compared principal component analysis (PCA) with sparse PCA (SPCA) as methods for transforming Rotation Forest. K-modes clustering with Levenshtein distance was used for the selection method of molecules, and CatBoost was used for the feature selection method. Results The amount of the DPP-4 inhibitor molecules resulting from the selection process of molecules using K-Modes clustering algorithm is 1020 with logP range value of -1.6693 to 4.99044. Several fingerprint methods such as extended connectivity fingerprint and functional class fingerprint with diameters of 4 and 6 were used to construct four fingerprint datasets, ECFP_4, ECFP_6, FCFP_4, and FCFP_6. There are 1024 features from the four fingerprint datasets that are then selected using the CatBoost method. CatBoost can represent QSAR models with good performance for machine learning and deep learning methods respectively with evaluation metrics, such as Sensitivity, Specificity, Accuracy, and Matthew’s correlation coefficient, all valued above 70% with a feature importance level of 60%, 70%, 80%, and 90%. Conclusion The K-modes clustering algorithm can produce a representative subset of DPP-4 inhibitor molecules. Feature selection in the fingerprint dataset using CatBoost is best used before making QSAR Classification and QSAR Regression models. QSAR Classification using Machine Learning and QSAR Classification using Deep Learning, each of which has an accuracy of above 70%. The QSAR RFC-PCA and QSAR RFR-PCA models performed better than QSAR RFC-SPCA and QSAR RFR-SPCA models because QSAR RFC-PCA and QSAR RFR-PCA models have more effective time than the QSAR RFC-SPCA and QSAR RFR-SPCA models.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

“…Additionally, in situations where 3-D structures of the target of interest are available, there has been demonstrated value in combining the outputs of these two different approaches [ 6 ]. In the last decade, both structure- and ligand-based methods have been improved with the use of machine learning (ML) [ 7 , 8 , 9 , 10 , 11 ]. This review will provide a brief introduction to these methods, including examples where they have been successfully utilized in radiopharmaceutical development to date.…”

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter

“…Drawing from a range of conventional classifiers and integrating insights from several successful QSAR machine learning projects − we present seven classification models for each of the three data sets: K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Decision Trees (DTs), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Extra Gradient Boost (XGBoost), and CatBoost. Table presents their comparative performance based on accuracy, sensitivity, and specificity.…”

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data

“…where v MAP represents the maximum posterior value; V represents the category set of the library composed of the daily FAQ library, the professional field FAQ library, the real-time question library, and the control instruction library; and the v j is the category that Q can be divided into [ 16 ]. Using Bayesian formula, formula ( 1 ) can be rewritten as …”