Quantitative structure-activity relationships (QSARs) are mathematical models aimed at finding a quantitative relationship between a set of chemical compounds and a specific activity or endpoint, such as toxicity, chemical or physical property, biological activity, and so on. In order to find out the correlation between the chemicals and the selected endpoints, QSAR models use the so-called molecular descriptors (MDs) which encode specific chemical information or features of the molecules. The early QSAR models were based on a small set of MDs and a specific endpoint, and the correlation was usually a linear mathematical correlation. However, nowadays, QSAR models are usually non-linear and made up by thousands of chemicals and hundreds of MDs. In addition, novel QSAR models are also aimed at the prediction of different endpoints with the same model, the so-called multi-target QSAR (MT-QSAR). Due to this, nowadays many QSARs are usually developed using machine learning approaches which can model a dataset with different endpoints. Although these approaches have demonstrated to be able to solve MT-QSAR models, feature selection (FS) in these cases is a challenging task and a main point in the QSAR field. Considering these aspects, the main aim of this chapter is to analyze feature selection methods while developing non-linear QSAR models. © Springer Science+Business Media, LLC, part of Springer Nature 2020.

The water gas shift (WGS) reaction is an important step in many industrial processes and has thus stimulated various investigations focusing on optimising its catalysts. Previous studies comparing the reactivity of pure and doped-metallic nanotubes towards the catalysis of water dissociation, the key rate-limiting step for the WGS reaction on copper surfaces, suggest that platinum nanotubes stand up as being probably the most active catalysts for the water gas shift reaction. Therefore, we present here a detailed analysis of the performance of platinum nanotubes in the catalysis of the WGS reaction, by employing the Pt(5,3) nanotube as catalyst model and periodic density functional theory calculations. To do so, several reaction pathways were considered on the faces of the Pt(5,3) nanotube and then, energetic balances for the elementary steps on each pathway were determined. This allowed us to conclude that the most feasible reaction route for the WGS reaction on this nanotube follows an associative mechanism through the carboxyl intermediary. The results of this study revealed also that the Pt(5,3) nanotube is an adequate system for the catalysis of the WGS reaction, apart from avoiding the sintering problem intrinsic to catalysts based on nanoparticles dispersed on a support.

By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to he predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.

<jats:title>Abstract</jats:title> <jats:p>Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling tools is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multi-target QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a <jats:italic>single</jats:italic> model equation, thus extending and improving the reliability of this type of modelling. We have developed <jats:italic>QSAR-Co-X</jats:italic>, an open source python−based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters along with graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, three case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched <jats:italic>QSAR-Co</jats:italic> code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.</jats:p>

Rapanone is a natural occurring benzoquinone with several biological effects including unclear cytotoxic mechanisms. Here we addressed if mitochondria are involved in the cytotoxicity of rapanone towards cancer cells by employing hepatic carcinoma (HepG2) cells and isolated rat liver mitochondria. In the HepG2, rapanone (20-40 mu M) induced a concentration-dependent mitochondria] membrane potential dissipation, ATP depletion, hydrogen peroxide generation and, phosphatidyl serine externalization; the latter being indicative of apoptosis induction. Rapanone toxicity towards primary rats hepatocytes (IC50 = 35.58 +/- 1.50 mu M) was lower than that found for HepG2 cells (IC50 = 27.89 +/- 0.75 mu M). Loading of isolated mitochondria with rapanone (5-20 mu M) caused a concentration-dependent inhibition of phosphorylating and uncoupled respirations supported by complex I (glutamate and malate) or the complex II (succinate) substrates, being the latter eliminated by complex IV substrate (TMPD/ascorbate). Rapanone also dissipated mitochondrial membrane potential, depleted ATP content, released Ca2+ from Ca2+ -loaded mitochondria, increased ROS generation, cytochrome c release and membrane fluidity. Further analysis demonstrated that rapanone prevented the cytochrome c reduction in the presence of decylbenzilquinol, identifying complex III as the site of its inhibitory action. Computational docking results of rapanone to cytochrome bc1 (Cyt bc1) complex from the human sources found spontaneous thermodynamic processes for the quinone-Q(o) and Q(i) binding interactions, supporting the experimental in vitro assays. Collectively, these observations suggest that rapanone impairs mitochondrial respiration by inhibiting electron transport chain at Complex III and promotes mitochondrial dysfunction. This property is potentially involved in rapanone toxicity on cancer cells.

In this work, one of the most prevalent polypharmacology drug-drug interaction events that occurs between two widely used beta-blocker drugs-i.e., acebutolol and propranolol-with the most abundant blood plasma fibrinogen protein was evaluated. Towards that end, molecular docking and Density Functional Theory (DFT) calculations were used as complementary tools. A fibrinogen crystallographic validation for the three best ranked binding-sites shows 100% of conformationally favored residues with total absence of restricted flexibility. From those three sites, results on both the binding-site druggability and ligand transport analysis-based free energy trajectories pointed out the most preferred biophysical environment site for drug-drug interactions. Furthermore, the total affinity for the stabilization of the drug-drug complexes was mostly influenced by steric energy contributions, based mainly on multiple hydrophobic contacts with critical residues (THR22: P and SER50: Q) in such best-ranked site. Additionally, the DFT calculations revealed that the beta-blocker drug-drug complexes have a spontaneous thermodynamic stabilization following the same affinity order obtained in the docking simulations, without covalent-bond formation between both interacting beta-blockers in the best-ranked site. Lastly, experimental ultrasound density and velocity measurements were performed and allowed us to validate and corroborate the computational obtained results.

The Biofilms Structural Database (BSD) is a collection of structural, mutagenesis, kinetics, and inhibition data to understand the processes involved in biofilm formation. Pre-sently, it includes curated infor-mation on 425 structures of proteins and enzymes involved in biofilm formation and development for 42 different bacteria.

P-glycoprotein (P-gp, ABCB1) overexpression is, currently, one of the most important multidrug resistance (MDR) mechanisms in tumor cells. Thus, modulating drug efflux by P-gp has become one of the most promising approaches to overcome MDR in cancer. Yet, more insights on the molecular basis of drug specificity and efflux-related signal transmission mechanism between the transmembrane domains (TMDs) and the nucleotide binding domains (NBDs) are needed to develop molecules with higher selectivity and efficacy. Starting from a murine P-gp crystallographic structure at the inward-facing conformation (PDB ID: 4Q9H), we evaluated the structural quality of the herein generated human P-gp homology model. This initial human P-gp model, in the presence of the "linker" and inserted in a suitable lipid bilayer, was refined through molecular dynamics simulations and thoroughly validated. The best human P-gp model was further used to study the effect of four single-point mutations located at the TMDs, experimentally related with changes in substrate specificity and drug-stimulated ATPase activity. Remarkably, each P-gp mutation is able to induce transmembrane alpha-helices (TMHs) repacking, affecting the drug-binding pocket volume and the drug-binding sites properties (e.g. volume, shape and polarity) finally compromising drug binding at the substrate binding sites. Furthermore, intracellular coupling helices (ICH) also play an important role since changes in the TMHs rearrangement are shown to have an impact in residue interactions at the ICH-NBD interfaces, suggesting that identified TMHs repacking affect TMD-NBD contacts and interfere with signal transmission from the TMDs to the NBDs.