Repositioning of Thiourea-Containing Drugs as Tyrosinase Inhibitors

Tyrosinase catalyzes two distinct sequential reactions in melanin biosynthesis: The hydroxylation of tyrosine to dihydroxyphenylalanine (DOPA) and the oxidation of DOPA to dopaquinone. Developing functional modulators of tyrosinase is important for therapeutic and cosmetic purposes. Given the abundance of thiourea moiety in known tyrosinase inhibitors, we studied other thiourea-containing drugs as potential tyrosinase inhibitors. The thiourea-containing drugs in clinical use were retrieved and tested for their ability to inhibit tyrosinase. We observed that methimazole, thiouracil, methylthiouracil, propylthiouracil, ambazone, and thioacetazone inhibited mushroom tyrosinase. Except for methimazole, there was limited information regarding the activity of other drugs against tyrosinase. Both thioacetazone and ambazone significantly inhibited tyrosinase, with IC₅₀ of 14 and 15 μM, respectively. Ambazone decreased melanin content without causing cellular toxicity at 20 μM in B16F10 cells. The activity of ambazone was stronger than that of kojic acid both in enzyme and melanin content assays. Kinetics of enzyme inhibition assigned the thiourea-containg drugs as non-competitive inhibitors. The complex models by docking simulation suggested that the intermolecular hydrogen bond via the nitrogen of thiourea and the contacts via thione were equally important for interacting with tyrosinase. These data were consistent with the results of enzyme assays with the analogues of thiourea.     

Comparison of 2D fingerprint methods for multiple-template similarity searching on compound activity classes of increasing structural diversity

We studied the similarity search performance of differently designed molecular fingerprints using multiple reference structures and different search strategies. For this purpose, nine compound activity classes were assembled that exclusively consisted of molecules with different core structures and that represented different levels of intra-class structural diversity. Thus, there was a strict one-to-one correspondence between test compounds and core structures. Analysis of unique core structures was found to be a better measure of class diversity than distributions of simplified scaffolds. On increasingly diverse classes, a trainable fingerprint using a unique search strategy performed better than others tested herein. Overall, clear preferences were detected for nearest-neighbor search strategies over fingerprint-averaging techniques. Nearest-neighbor searching that relied on selecting database compounds most similar to one of the reference structures often improved compound recovery over other averaging methods, but at the cost of decreasing the ability to detect hits that were structurally distinct from reference molecules.     

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.     

An Inverse QSAR Method Based on a Two-Layered Model and Integer Programming

A novel framework for inverse quantitative structure–activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treated by the framework are limited. In order to deal with an arbitrary graph in the framework, we introduce a new model, called a two-layered model, and develop a corresponding method. In this model, each chemical graph is regarded as two parts: the exterior and the interior. The exterior consists of maximal acyclic induced subgraphs with bounded height, the interior is the connected subgraph obtained by ignoring the exterior, and the feature vector consists of the frequency of adjacent atom pairs in the interior and the frequency of chemical acyclic graphs in the exterior. Our method is more flexible than the existing method in the sense that any type of graphs can be inferred. We compared the proposed method with an existing method using several data sets obtained from PubChem database. The new method could infer more general chemical graphs with up to 50 non-hydrogen atoms. The proposed inverse QSAR method can be applied to the inference of more general chemical graphs than before.     

Smart Design of Small-Molecule Libraries: When Organic Synthesis Meets Cheminformatics

Synthetic chemists are always looking for new methods to maximize the diversity and complexity of small-molecule libraries. Diversity-oriented synthesis can give access to new chemotypes with high chemical diversity, exploiting complexity-generating reactions and divergent approaches. However, there is a need for new tools to drive synthetic efforts towards unexplored and biologically relevant regions of chemical space. Because the number of publicly accessible biological data will increase in the years to come, cheminformatics can represent a real opportunity to develop better chemical libraries. This minireview focuses on novel cheminformatics approaches used to design molecular scaffolds, as well as to analyze their quality, giving a perspective of them in the field of chemical biology and drug discovery through some selected case studies.     

Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

Cheminformatics plays a vital role in maintaining large amounts of chemical data. The reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and the manufacturing of chemical compounds. Toxicity prediction requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; the computational demands of such techniques increase greatly with the number of chemical compounds involved. State‐of‐the‐art prediction methods such as neural networks and multilayer regression require either tuning parameters or complex transformations of predictor or outcome variables and do not achieve highly accurate results. This paper proposes a quantum‐inspired genetic programming model to improve prediction accuracy. Genetic programming is utilized to give a linear equation for calculating the degree of toxicity more accurately. Quantum computing is employed to improve the selection of the best‐of‐run individuals and handles parsimony pressure to reduce the complexity of solutions. The results of the internal validation analysis indicated that the quantum‐inspired genetic programming model has better goodness‐of‐fit statistics then and significantly outperforms the neural network model.     

A Deep Learning-Based Quantitative Structure–Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.