SAR and QSAR research on tyrosinase inhibitors using machine learning methods. Issue 2 (1st February 2021)

• Record Type: Journal Article
• Authors: Wu, Y.; Huo, D.; Chen, G.; Yan, A.
• Is Part Of: SAR and QSAR in environmental research. Volume 32:Issue 2(2021)
• Page Start: 85
• Page End: 110
• Publication Date: 2021-02-01
• Subjects: Structure-activity relationship (SAR) -- quantitative structure-activity relationship (QSAR) -- K-Means -- tyrosinase inhibitors -- machine learning; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2020.1862297
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods to develop 15 classification models. Model 5B built by fully connected neural networks and ECFP4 fingerprints achieved the highest prediction accuracy of 91.36% and Matthews correlation coefficient (MCC) of 0.81 on the test set. The applicability domains (AD) of classification models were defined by d S T D − P R O method. Moreover, we clustered the 1097 inhibitors into eight subsets by K-Means to figure out inhibitors' structural features. In addition, 10 quantitative structure–activity relationship (QSAR) models were constructed by four machine learning methods based on 813 inhibitors. Model 6 J, the best QSAR model, was developed by fully connected neural networks with 50 RDKit descriptors. It resulted in a coefficient of determination ( r 2 ) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors.