With the recent crystallization of several G Protein-Coupled receptors (GPCRs), homology modelling and all atom molecular dynamics (MD) simulations have proven their usefulness for exploring the structure and function of this superfamily of membrane receptors. Subsequently, automated computational protocols have been implemented as web-based servers in the recent years to produce reliable models of GPCRs, providing partial or global solutions for the structural characterization and molecular simulation of GPCRs. These dedicated modelling services represent an attractive tool for the broader community of public researchers and pharmaceutical companies, in order to assist in the structure-based drug design of GPCRs. We here collect and analyze the existing web servers, among which a previously unreported service, GPCR-ModSim, offers for the first time full atom MD simulations in the pipeline for GPCR molecular modelling.

Variable selection is important in QSAR modeling since it can improve model performance and transparency, as well as reduce the computational cost of model fitting and predictions. Which variable selection methods that perform well in QSAR settings is largely unknown. To address this question we, in a total of 1728 benchmarking experiments, rigorously investigated how eight variable selection methods affect the predictive performance and transparency of random forest models fitted to seven QSAR datasets covering different endpoints, descriptors sets, types of response variables, and number of chemical compounds. The results show that univariate variable selection methods are suboptimal and that the number of variables in the benchmarked datasets can be reduced with about 60 % without significant loss in model performance when using multivariate adaptive regression splines MARS and forward selection.

This work establishes a Quantitative Structure-Property Relationships (QSPR) based analysis with the aim of interpreting both the structural and electronic properties of the polar region of valproic acid and its derivatives, in terms of stabilizing intramolecular interactions related to the involved substituents. We consider ten different calculated properties as dependent variables for the QSPR models: the bond lengths C₈O₉, C₈X₁₀, and the percentage of s-character of the natural hybrids forming the bonding σ orbitals of the O₉C₈X₁₀ region. The representative descriptors are the charges transferred during donor/acceptor interactions around this function calculated at the B3LYP/6-311++G**(6d,10f) level of theory, and/or hybrid descriptors derived therefrom. The models so established result simple, predictive, and have a quite direct physical meaning.

Metabolic pathways are at the crossroad between the chemical world of small molecules and the biological world of enzymes, genes and regulation. Methods for their processing are therefore required for a great variety of applications. The work presented here reports a new method to encode metabolic pathways and reactomes of organisms based on the MOLMAP approach. Pathways are represented from features of the metabolites involved in their reactions enabling to automatically perceive chemical similarities, and making no use of EC numbers. MOLMAP descriptors are based on atomic topological and physicochemical features of the bonds involved in reactions. The results show that self-organizing maps (SOM) can be trained with MOLMAPs of pathways to automatically recognize similarities between pathways of the same type of metabolism. The study also illustrates the possibility of applying the MOLMAP methodology at progressively higher levels of complexity, bridging chemical and biological information, and going all the way from atomic properties to the classification of organisms.

Nitrosative and oxidative stress, associated with the generation of excessive reactive nitrogen and oxygen radical species respectively, are thought to contribute to protein misfolding diseases which represent a group of neurodegenerative disorders that are characterized by protein aggregation and plaque formation. Curcumin, a polyphenolic compound, possesses diverse anti-inflammatory, antitumor, and antioxidant properties. Several studies have revealed that curcumin can reduce the oxidative/nitrosative stress and thereby decrease the neuronal attrition. However, curcumin has poor bioavailability and has raised several concerns focused on its limited clinical impact. The aim of this study was to find other compounds which can assist in decreasing nitrosative stress and possess enhanced bioavailability. Here, use of β-lactoglobulin was examined as a vehicle to transport molecules to the gut. The Zinc database was searched using curcumin as reference and 6457 compounds were selected for the study. These compounds were docked to β-lactoglobulin using Glide to find the best fit ligands. Our studies identified four compounds that bind to β-lactoglobulin and scavenge NOx (free radicals) efficiently.

Recent research has focused on algorithms to derive numerical measures of selectivity based on panels of in vitro pharmacology assays so that one molecule’s activity profile may be compared easily with that of another. However, the questions concerning which method or algorithm is best to use, the optimal number of assays required to give an accurate measure of selectivity and the correlation of these measures to in vivo toxicity have remained largely unexplored. In this manuscript we describe a systematic approach to compare and contrast different calculation methods for promiscuity and determine the optimal number and constitution of a panel of assays to measure the selectivity/promiscuity of compounds across all targets. We then go on to examine their relationship to toxicity using a Pfizer proprietary compound set that has both selectivity profiles and exploratory toxicology study results. From this study we conclude that all five methods studied are useful in estimating compound selectivity; that a small panel of between 15 to 30 binding assays can be used as a surrogate for a broader panel enabling higher throughput with lower costs and this panel will most likely have the highest prediction power when correlating this measure to in vivo effects.

A spherical self-organizing map (SSOM) was applied for mapping the molecular surface of a protein structure on a spherical structure. The active site of the X-ray crystal structure of β2 receptor protein was used for this purpose. After mapping the molecular surface points and assigning the associated molecular electrostatic potential (MEP) values, the original 3D structure of the active site was well reproduced by the SSOM. In order to validate the geometrical transformation and the resulting MEP distribution, the molecular surfaces of twenty β2 ligands were mapped on the established SSOM sphere. The MEP values of two spheres derived from the ligand and the β2 receptor protein were compared. In almost all cases of strong ligands, the two spheres had a moderate negative correlation.

G protein-coupled receptors (GPCRs) constitute the largest family of membrane-bound receptors with more than 800 members encoded by 351 genes in humans. It has been estimated that more than 50 % of clinically available drugs act on GPCRs, with an amount of 400, 50 and 25 druggable proteins for the class A, B and C, respectively. Furthermore, Class A GPCRs with approximately 25 % of marketed small drugs represent the most attractive pharmaceutical class. The recent availability of high-resolution 3-dimensional structures of some GPCRs supports the notion that GPCRs are dynamically versatile, and their functions can be modulated by several factors. In this scenario, molecular dynamics (MD) simulations techniques appear to be crucial when studying GPCR flexibility associated to functioning and ligand recognition. A general overview of biased and unbiased MD techniques is here presented with special emphasis on the recent results obtained in the GPCRs field.

Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors.

Detailed knowledge of how molecules recognize interaction partners and of the conformational preferences of biomacromolecules is pivotal for understanding biochemical processes. Such knowledge also provides the foundation for the design of novel molecules, as undertaken in pharmaceutical research. Computer-based free energy calculations enable a detailed investigation of the energetic factors that are responsible for molecular stability or binding affinity. The Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) approach is an efficient method for the calculation of free energies of diverse molecular systems. Here we describe the concepts of this approach and outline the practical proceeding. Furthermore we give an overview of the wide spectrum of problems that have been addressed with this method and of successful analyses carried out, thereby focussing on ambitious and recent studies. Limits of the approach in terms of accuracy and applicability are discussed. Despite these limitations MM-PBSA is a method with great potential that allows comparative free energy analyses for various molecular systems at low computational cost.

Molecular Informatics publishes research that will deepen our understanding about information storage and processing on the molecular level, signaling and regulation of biological and chemical systems including cellular systems and macromolecular assemblies, modeling of molecular interactions and networks, and the design of molecular modulators that exhibit desired biochemical and pharmacological effects. Various aspects of this transdisciplinary scientific area are depicted on the cover: Cells with their nuclei and membranes (image courtesy of Dr. A. Schreiner and E. Resch), models of receptor-ligand interactions, and an artistic representation of “biological information” as multiple bit-codes presented on a right-handed helix.

Protein-protein interactions (PPIs) play a central and crucial role in almost every cellular process. Understanding the structural basis of protein-protein interactions can lead to the development of new drugs for treatment of various diseases. With this purpose, peptide-based drug design (PBDD) has been extensively explored in the last few decades. Peptidomimetics are compounds which mimic the biological activity of peptides while offering the advantages of improving their pharmacokinetics profiles. In this review, we would like to summarize the state of the art of computational methods which have been recently introduced to design novel peptidomimetics involved in a therapeutically relevant protein-protein recognition processes.

Toxic myopathy is a muscular disease in which the muscle fibers do not function and which results in muscular weakness. Some drugs, such as lipid-lowering drugs and antihistamines, can cause toxic myopathy. In this work, a dataset containing 232 chemical compounds inducing toxic myopathy (IM-compounds) and 117 drugs not inducing toxic myopathy (notIM-compounds) was collected. The dataset was split into a training set (containing 270 compounds) and a test set (containing 79 compounds). A Kohonen’s self-organizing map (SOM) and a support vector machine (SVM) were applied to develop classification models to differentiate IM-compounds and notIM-compounds. Polarizibity related descriptors, electronegativity related descriptors, atom charges related descriptors, H-bonding related descriptor, atom identity and molecular shape descriptors were used to build models. Using the SOM method, classification accuracies of 88.4 % for the training set and 88.2 % for the test set were achieved; using the SVM method, classification accuracies of 95.6 % for the training set and 86.1 % for the test set were achieved. In addition, extended connectivity fingerprints (ECFP_4) were calculated and analyzed to find important substructures of molecules relating to toxic myopathy.

Today’s chemical processes involve many components, and it is necessary to know their basic physical properties for process design and operation. However, it is not always possible to find the property information of all components in the literature. Generally, there are two ways to evaluate properties of chemical compounds when they do not exist in the literature: the experimental measurement and predictive approaches based on empirical models. The latter is called the group contribution method (GCM), and its basic concept is that specific functional groups or fragments of a molecule contribute to the value of its physical property. The advantage of the GCMs is that they reduce the effort and cost compared to experiments. This study proposes a novel GCM method suitable for high-dimensional, sparse data sets. In order to improve its applicability and accuracy, the database is extended and divided into non-ring group compounds and ring group ones. Support vector regression (SVR) is adopted as the regression model, and a derivative-free optimization approach, referred to as particle swarm optimization, is incorporated into the parameter optimization step in learning the SVM model to avoid local optimality. Performance of the proposed model is compared to those of other GCMs.

Cytochrome P450 (CYP) is an important drug-metabolizing enzyme family. Different CYPs often have different substrate preferences. In addition, one drug molecule may be preferentially metabolized by one or more CYP enzymes. Therefore, the classification and prediction of substrate specificity of CYP enzymes are of importance to the understanding of drug metabolisms and may help guide the development of new drugs. In this study, we used three different machine learning methods to classify CYP substrates for predicting CYP-substrate specificity based solely on structural and physicochemical properties of the substrates. We first built a simple decision tree model to classify substrates of four CYP enzymes, 1A2, 2C9, 2D6 and 3A4 with more than 78 % classification accuracy. We then built a single-label eight-class model and a multilabel five-class model to classify substrates of eight CYP enzymes and to classify substrates that can be metabolized by more than one CYP enzymes, respectively. Above 90 % and >80 % prediction accuracy was achieved for the single-label and multilabel models, respectively. The main improvement of our models over existing ones is the automated and unbiased selection of descriptors by genetic algorithms, which makes our methods applicable for larger data sets and increased number of CYP enzymes.

A total of 6289 drug-like anticancer molecules were collected from Binding database and were analyzed by using the classification techniques. The collected molecules were encoded to a diverse set of descriptors, spanning different physical and chemical properties of the molecules. A combination of genetic algorithms and counterpropagation artificial neural networks was used for navigating the generated drug-like chemical space and selecting the most relevant molecular descriptors. The proposed method was used for the classification of the molecules according to their therapeutic targets and activities. The selected molecular descriptors in this work define discrete areas in chemical space, which are mainly occupied by particular classes of anticancer molecules. The obtained structure-activity relationship (SAR) patterns and classification rules contain valuable information, which help to screen the large databases of compounds, more precisely. Such rules and patterns can be considered as virtual filters for mining the large databases of compounds and are useful in finding new anticancer candidates.

Descriptor properties are often neglected when building 3D-QSAR models. The relevance of correlation and distribution profiles is tested in terms of the models’ prediction power. A different approach to filter descriptors prior to variable selection is proposed. Additionally, a protocol for molecular interaction field descriptors selection and model validation is presented. The algorithms and protocols presented are quite simple and enable a different and powerful way to create parsimonious interaction field-based models.

Upon binding to a receptor, agonists and antagonists can induce distinct biological functions and thus lead to significantly different pharmacological responses. Thus, in silico prediction or in vitro characterization of ligand agonistic or antagonistic functionalities is an important step toward identifying specific pharmacological therapeutics. In this study, we investigated the molecular properties of agonists and antagonists of human 5-hydroxytryptamine receptor subtype 1A (5-HT_1A). Subsequently, intrinsic functions of these ligands (agonists/antagonists) were modeled by support vector machine (SVM), using five 2D molecular fingerprints and the 3D Topomer distance. Five kernel functions, including linear, polynomial, RBF, Tanimoto and a novel Topomer kernel based on Topomer 3D similarity were used to develop linear and nonlinear classifiers. These classifiers were validated through cross-validation, yielding a classification accuracy ranging from 80.4 % to 92.3 %. The performance of different kernels and fingerprints was analyzed and discussed. Linear and nonlinear models were further interpreted through the illustration of underlying classification mechanism. The computation protocol has been automated and demonstrated through our online service. This study expands the scope and applicability of similarity-based methods in cheminformatics, which are typically used for the identification of active molecules against a target protein. Our findings provide a good starting point for further systematic classifications of other GPCR ligands and for the data mining of large chemical libraries.