Synthesis, Biological Activity and Molecular Docking Studies of Novel Sulfonate Derivatives Bearing Salicylaldehyde

Enzyme activity alterations have been associated with many metabolism disorders and have crucial roles in the pathogenesis of some diseases. Tyrosinase is a key enzyme in melanin biosynthesis, which is responsible for skin pigmentation to protect the skin from solar radiation. Pancreatic lipase has been considered a key enzyme for the treatment of obesity. Herein, we reported the synthesis and enzyme inhibitions of a series of sulfonates as possible tyrosinase and pancreatic lipase inhibitors. According to the calculated IC₅₀ values, compound 3f (74.1±11.1 μM) and compound 3c (86.6±6.9 μM) were determined to be the best inhibitors among the synthesized compounds for the tyrosinase and pancreatic lipase enzymes, respectively. The approach yielded at extremely high level by creating very flexible structural domains for the chemically modified groups. The structural characterization of the target molecules was implemented by ¹ H-NMR, ¹³ C-NMR, and HR-MS analyses. Also, molecular docking studies of the synthesized compounds with tyrosinase and pancreatic lipase enzymes were conducted using AutoDock Vina software. Additionally, the studies of the absorption distribution, metabolism, and excretion (ADME) were performed to uncover the target compounds' pharmacokinetics, drug similarities, and medicinal properties of the novel sulfonate derivatives bearing salicylaldehyde.

SAR and QSAR research on tyrosinase inhibitors using machine learning methods

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.

A systematic review of synthetic tyrosinase inhibitors and their structure-activity relationship

Tyrosinase is a copper-containing oxidation enzyme, which is responsible for the production of melanin. This enzyme is widely distributed in microorganisms, animals and plants, and plays an essential role in undesirable browning of fruits and vegetables, antibiotic resistance, skin pigment formation, sclerotization of cuticle, neurodegeneration, etc. Hence, it has been recognized as a therapeutic target for the development of antibrowning agents, antibacterial agents, skin-whitening agents, insecticides, and other therapeutic agents. With great potential application in food, agricultural, cosmetic and pharmaceutical industries, a large number of synthetic tyrosinase inhibitors have been widely reported in recent years. In this review, we systematically summarized the advances of synthetic tyrosinase inhibitors in the literatures, including their inhibitory activity, cytotoxicity, structure-activity relationship (SAR), inhibition kinetics, and interaction mechanisms with the enzyme. The collected information is expected to provide a rational guidance and effective strategy to develop novel, potent and safe tyrosinase inhibitors for better practical applications in the future.