Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor-ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.

In this study, the impact of four P-gp mutations (G185V, G830V, F978A and Delta F335) on drug-binding and efflux-related signal-transmission mechanism was comprehensively evaluated in the presence of ligands within the drug-binding pocket (DBP), experimentally related with changes in their drug efflux profiles. The severe repacking of the transmembrane helices (TMH), induced by mutations and exacerbated by the presence of ligands, indicates that P-gp is sensitive to perturbations in the transmembrane region. Alterations on drug-binding were also observed as a consequence of the TMH repacking, but were not always correlated with alterations on ligands binding mode and/or binding affinity. Finally, and although all P-gp variants holo systems showed considerable changes in the intracellular coupling helices/nucleotide-binding domain (ICH-NBD) interactions, they seem to be primarily induced by the mutation itself rather than by the presence of ligands within the DBP. The data further suggest that the changes in drug efflux experimentally reported are mostly related with changes on drug specificity rather than effects on signal-transmission mechanism. We also hypothesize that an increase in the drug-binding affinity may also be related with the decreased drug efflux, while minor changes in binding affinities are possibly related with the increased drug efflux observed in transfected cells.Communicated by Ramaswamy H. Sarma

We present a comprehensive review of methods and applications of molecular simulations of interfacial systems. We give a detailed overview of the main techniques and major challenges in the following aspects of solid and fluid surfaces: adsorption at solid surfaces, interfacial transport and surface-to-bulk partitioning. We summarise methods to estimate macroscopic properties interfaces (adsorption isotherms, surface tension and contact angle) and ways to extract quantitative information about fluctuating liquid surfaces. We demonstrate the usage of these methods on recent applications from the fields of atmospheric science, material science and biophysics. The two main goals of this review are: (i) to provide guidance in practical questions, such as choosing software, force field, level of theory, system geometry, and finding the appropriate selective surface analysis methods based on the type of the interface and the nature of the physical problem to be studied; and (ii) to highlight domains where molecular simulations can bring about substantial advances in our understanding in important questions of applied science as a function of future method development and adaptation for applied fields.

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity to-wards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the manage-ment of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.

Introduction: Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations.Areas covered: The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process.Expert opinion: Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

Manufactured substances known as endocrine disrupting chemicals (EDCs) released in the environment, through the use of cosmetic products or pesticides, can cause severe eco and cytotoxicity that may induce trans-generational as well as long-term deleterious effects on several biological species at relatively low doses, unlike other classical toxins. As the need for effective, affordable and fast EDCs environmental risk assessment has become increasingly pressing, the present work introduces the first moving average-based multitasking quantitative structure-toxicity relationship (MA-mtk QSTR) modeling specifically developed for predicting the ecotoxicity of EDCs against 170 biological species belonging to six groups. Based on 2,301 data-points with high structural and experimental diversity, as well as on the usage of various advanced machine learning methods, the novel most predictive QSTR models display overall accuracies > 87% in both training and prediction sets. However, maximum external predictivity was achieved when a new multitasking consensus modeling approach was applied to these models. Additionally, the developed linear model provided means to investigate the determining factors for eliciting higher ecotoxicity by the EDCs towards different biological species, identifying several factors such as solvation, molecular mass and surface area as well as the number of specific molecular fragments (e.g.: aromatic hydroxy and aliphatic aldehyde). The resource to non-commercial open-access tools to develop the models is a useful step towards library screening to speed up regulatory decision on discovery of safe alternatives to reduce the hazards of EDCs.

A medicinal chemistry approach combining in silico and in vitro methodologies was performed aiming at identifying and characterizing putative allosteric drug-binding sites (aDBSs) at the interface of the transmembrane-and nucleotide-binding domains (TMD-NBD) of P-glycoprotein. Two aDBSs were identified, one in TMD1/NBD1 and another one in TMD2/NBD2, by means of in silico fragment-based molecular dynamics and characterized in terms of size, polarity, and lining residues. From a small library of thioxanthone and flavanone derivatives, experimentally described to bind at the TMD-NBD interfaces, several compounds were identified to be able to decrease the verapamil-stimulated ATPase activity. An IC50 of 81 +/- 6.6 mu M is reported for a flavanone derivative in the ATPase assays, providing evidence for an allosteric efux modulation in P-glycoprotein. Molecular docking and molecular dynamics gave additional insights on the binding mode on how flavanone derivatives may act as allosteric inhibitors.