Application of 3D-QSAR in the Rational Design of Receptor Ligands and Enzyme Inhibitors

MolPredictX: Online Biological Activity Predictions by Machine Learning Models

Here we report the development of MolPredictX, an innovate and freely accessible web interface for biological activity predictions of query molecules. MolPredictX utilizes in-house QSAR models to provide 27 qualitative predictions (active or inactive), and quantitative probabilities for bioactivity against parasitic (Trypanosoma and Leishmania), viral (Dengue, Sars-CoV and Hepatitis C), pathogenic yeast (Candida albicans), bacterial (Salmonella enterica and Escherichia coli), and Alzheimer disease enzymes. In this article, we introduce the methodology and usability of this webtool, highlighting its potential role in the development of new drugs against a variety of diseases. MolPredictX is undergoing continuous development and is freely available at https://www.molpredictx.ufpb.br/.

Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

Comparative Analysis of Virtual Screening Approaches in the Search for Novel EphA2 Receptor Antagonists

The EphA2 receptor and its ephrin-A1 ligand form a key cell communication system, which has been found overexpressed in many cancer types and involved in tumor growth. Recent medicinal chemistry efforts have identified bile acid derivatives as low micromolar binders of the EphA2 receptor. However, these compounds suffer from poor physicochemical properties, hampering their use in vivo. The identification of compounds able to disrupt the EphA2-ephrin-A1 complex lacking the bile acid scaffold may lead to new pharmacological tools suitable for in vivo studies. To identify the most promising virtual screening (VS) protocol aimed at finding novel EphA2 antagonists, we investigated the ability of both ligand-based and structure-based approaches to retrieve known EphA2 antagonists from libraries of decoys with similar molecular properties. While ligand-based VSs were conducted using UniPR129 and ephrin-A1 ligand as reference structures, structure-based VSs were performed with Glide, using the X-ray structure of the EphA2 receptor/ephrin-A1 complex. A comparison of enrichment factors showed that ligand-based approaches outperformed the structure-based ones, suggesting ligand-based methods using the G-H loop of ephrin-A1 ligand as template as the most promising protocols to search for novel EphA2 antagonists.

Synthesis and quantitative structure-activity relationship of fatty acid amide hydrolase inhibitors: modulation at the N-portion of biphenyl-3-yl alkylcarbamates

Alkylcarbamic acid biphenyl-3-yl esters are a class of fatty acid amide hydrolase (FAAH) inhibitors that comprises cyclohexylcarbamic acid 3'-carbamoylbiphenyl-3-yl ester (URB597), a compound with analgesic, anxiolytic-like and antidepressant-like properties in rat and mouse models. Here, we extended the structure-activity relationships (SARs) for this class of compounds by replacing the cyclohexyl ring of the parent compound cyclohexylcarbamic acid biphenyl-3-yl ester (URB524) (FAAH IC50 = 63 nM) with a selected set of substituents of different size, shape, flexibility, and lipophilicity. Docking experiments and linear interaction energy (LIE) calculations indicated that the N-terminal group of O-arylcarbamates fits within the lipophilic region of the substrate-binding site, mimicking the arachidonoyl chain of anandamide. Significant potency improvements were observed for the beta-naphthylmethyl derivative 4q (IC50 = 5.3 nM) and its 3'-carbamoylbiphenyl-3-yl ester 4z (URB880, IC50 = 0.63 nM), indicating that shape complementarity and hydrogen bonds are crucial to obtain highly potent inhibitors.

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.

An improved synthesis of cis-4-phenyl-2-propionamidotetralin (4-P-PDOT): a selective MT(2) melatonin receptor antagonist

A novel, efficient and diastereoselective procedure was developed for the gram-scale synthesis of cis-4-phenyl-2-propionamidotetralin (4-P-PDOT), a selective MT(2) melatonin receptor antagonist. The synthetic strategy involved the conversion of 4-phenyl-2-tetralone to enamide followed by diastereoselective reduction affording cis-4-P-PDOT in good yield. The mechanism of the reduction step was explored by employing deuterated reagents.