Quantitative structure activity relationship (QSAR) modeling is a well-known in silico technique with extensive applications in several major fields such as drug design, predictive toxicology, materials science, food science, etc. Handling small-sized datasets due to the lack of experimental data for specialized end points is a crucial task for the QSAR researcher. In the present study, we propose an integrated workflow/scheme capable of dealing with small dataset modeling that integrates dataset curation, "exhaustive" double cross-validation and a set of optimal model selection techniques including consensus predictions. We have developed two software tools, namely, Small Dataset Curator, version 1.0.0, and Small Dataset Modeler, version 1.0.0, to effortlessly execute the proposed workflow. These tools are freely available for download from https://dtclab.webs.com/software-tools. We have performed case studies employing seven diverse datasets to demonstrate the performance of the proposed scheme (including data curation) for small dataset QSAR modeling. The case studies also confirm the usability and stability of the developed software tools.

Molecular dynamics simulations of the electrical double layer at electrode-ionic liquid interfaces allow for molecular level interpretation of the interfacial phenomena and properties, such as differential capacitance (C). In this work, we have simulated an ionic liquid - 1-butyl-3-methylimidazolium hexafluorophosphate - at three gold surfaces, namely: Au(100), Au(110), and Au(111) surfaces. Atomic corrugation of the gold surface leads to higher C values due to the rapprochement of the surface and electrolyte charge planes. Likewise, by accounting for the shift of surface charge plane position towards the electrolyte also results in higher C values. The presented insight shows that a simple correction to the simulation data improves the agreement with the experimental data.

Because of the increasing demand of greener solvents, deep eutectic solvents (DES) have just emerged as low-cost alternative solvents for a broad range of applications. However, recent toxicity assay studies showed a non-negligible toxic behavior for these solvents and their components. Alternative in silico-based approaches such as the one proposed here, multitasking-Quantitative Structure Toxicity Relationships (mtk-QSTR), are increasingly used for risk assessment of chemicals to speed up policy decisions. This work reports a mtk-QSTR modeling of 572 DES and their components under multiple experimental conditions. To set up a reliable model from such data, we examined here the use of 0D-2D descriptors along with classification analysis, and the Box-Jenkins approach. This procedure led to a final mtk-QSTR model with high overall accuracy and predictivity (ca. 90%). The model highlights also the crucial role that polarizability, electronegativity, hydrogen-bond donor (HBD), and topological properties play into the DES toxicity. Furthermore, with the help of the derived mtk-QSTR model, 30 different HBD components were ranked on the basis of their toxic contributions to DES. More importantly, the proposed in silico modeling approach is shown to be a valuable tool to mine relevant STR information, therefore guiding the rational design of potentially safe DES.

Predicting enzyme function and enzyme subclasses is always a key objective in fields such as biotechnology, biochemistry, medicinal chemistry, physiology, and so on. The Protein Data Bank (PDB) is the largest information archive of biological macromolecular structures, with more than 150 000 entries for proteins, nucleic acids, and complex assemblies. Among these entries, there are more than 4000 proteins whose functions remain unknown because no detectable homology to proteins whose functions are known has been found. The problem is that our ability to isolate proteins and identify their sequences far exceeds our ability to assign them a defined function. As a result, there is a growing interest in this topic, and several methods have been developed to identify protein function based on these innovative approaches. In this work, we have applied perturbation theory to an original data set consisting of 19 187 enzymes representing all 59 subclasses present in the protein data bank. In addition, we developed a series of artificial neural network models able to predict enzyme-enzyme pairs of query-template sequences with accuracy, specificity, and sensitivity greater than 90% in both training and validation series. As a likely application of this methodology and to further validate our approach, we used our novel model to predict a set of enzymes belonging to the yeast Pichia stipites. This yeast has been widely studied because it is commonly present in nature and produces a high ethanol yield by converting lignocellulosic biomass into bioethanol through the xylose reductase enzyme. Using this premise, we tested our model on 222 enzymes including xylose reductase, that is, the enzyme responsible for the conversion of biomass into bioethanol.

Quantitative structure activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites. google.com/view/qsar-co) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

Recently, water transport through graphene oxide (GO) nanochannels has gained much attention because of its potential applications in desalination and water filtration. In this work, molecular dynamics (MD) simulations were carried out to elucidate the filtration efficiency and water structure in GO nanochannels of varying oxidation degree (from 0% to 40%) and interlayer spacing (from 0.6 nm to 1.0 nm). The results from these simulations show that ion permeation is observed in 1.0 nm channels, but ion rejection is close to complete for the narrower channels, irrespective of the degree of oxidation. Furthermore, water permeation increases with increasing interlayer separation and decreases with increasing oxidation degree. In general, water molecules prefer flowing through unoxidized regions of the narrower channels, but show no preferential path when flowing through the wider ones. Moreover, the analysis of the interaction between water molecules and the hydroxyl groups inside the GO channel shows that OH groups play a vital role disrupting the solvation sphere of the salt ions. However, with increasing oxidation of the GO wall, the formation of intra-layer hydrogen bonds becomes relevant, decreasing the overall number of hydrogen bonds involving water molecules, and thus preventing further decrease of the water permeance. To sum up, our results indicate that the best GO channel for the desalination is the one with interlayer spacing 0.8 nm and oxidation degree of 10% or lower.

We present a computational analysis coupled with experimental studies, focusing on the binding-interaction between beta-adrenoreceptor blocking agents (acebutolol and propranolol) with fibrinogen protein (E-region). Herein, computational modeling on structural validation and flexibility properties of fibrinogen E-region showed that the E-region interacting residues, which form the funnel-shaped hydrophobic cavity for ligand-binding, can be efficiently modeled. The obtained free energy of binding (FEB) values for the docking complexes, namely acebutolol/fibrinogen E-region and propranolol/fibrinogen E-region, were very close and amounted to 6.9 kcal/mol and - 6.8 kcal/mol, respectively. They were supported by a high binding-accuracy (R.M.S.D < 2 angstrom) for the best crystallographic binding-poses in both cases. In this regard, we identify a docking-mechanism of interaction for the propranolol and acebutolol mainly based on non-covalent hydrophobic contacts with the fibrinogen E-region binding-site. Besides, the beta-adrenoreceptor blocking agents are able to induce local perturbations affecting particularly the fibrinogen E-region allosteric residues linked to significant changes in the inter-residue communication and flexibility properties of residue network. In this sense, we show that the key biophysical parameters like frequency and collectivity degree may be compromised in different ways by the interaction with acebutolol and propranolol. Isothermal titration calorimetry, zeta potential and small angle X-ray scattering (SAXS) measurements were performed to complete and corroborate computational analysis. The combined experimental results point out that acebutolol acts to a lesser extent to fibrinogen structure than propranolol.

Diabetes Mellitus (DM) and Alzheimer's disease (AD) are two prevalent diseases in modern societies, which arc caused mainly by current lifestyle, aging and genetic alterations. It has already been demonstrated that these two diseases are associated, since individuals suffering from DM are prone to develop AD. Conversely, it is also known that individuals with AD are more susceptible to DM, namely type 2 diabetes (T2DM). Therefore, these two pathologies, although completely different in terms of symptomatology, end up sharing several mechanisms at the molecular level, with the most obvious being the increase of oxidative stress and inflammation. Polyphenols are natural compounds widely spread in fruits and vegetables whose dietary intake has been considered inversely proportional to the incidence of DM and AD. So, it is believed that this group of phytochemicals may have preventive and therapeutic potential, not only by reducing the risk and delaying the development of these pathologies, but also by improving brain's metabolic profile and cognitive function. The aim of this review is to understand the extent to which DM and AD are related pathologies, the degree of similarity and the relationship between them, to detail the molecular mechanisms by which polyphenols may exert a protective effect, such as antioxidant and anti-inflammatory effects, and highlight possible advantages of their use as common preventive and therapeutic alternatives.