Hepatitis C constitutes an unresolved global health problem. This infectious disease is caused by the hepatotropic hepatitis C virus (HCV), and it can lead to the occurrence of life-threatening medical conditions, such as cirrhosis and liver cancer. Nowadays, major clinical concerns have arisen because of the appearance of multidrug resistance (MDR) and the side effects especially associated with long-term treatments. In this work, we report the first multitasking model for quantitative structure-biological effect relationships (mtk-QSBER), focused on the simultaneous exploration of anti-HCV activity and in vitro safety profiles related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The mtk-QSBER model was created from a data set formed by 40 158 cases, displaying accuracy higher than 95% in both training and prediction (test) sets. Several molecular fragments were selected, and their quantitative contributions to anti-HCV activity and ADMET profiles were calculated. By combining the analysis of the fragments with positive contributions and the physicochemical meanings of the different molecular descriptors in the mtk-QSBER, six new molecules were designed. These new molecules were predicted to exhibit potent anti-HCV activity and desirable in vitro ADMET properties. In addition, the designed molecules have good druglikeness according to the Lipinski's rule of five and its variants.

In this chapter, we propose a novel computational methodology for the virtual design and screening of peptides with potential anticancer activity against different cancer cell lines, and low cytotoxicity against diverse healthy mammalian cells. In this context, a multitasking (mtk) chemoinformatic model combining Broto-Moreau autocorrelations with artificial neural networks was derived from a data set containing 1933 cases of peptides. The model exhibited an accuracy greater than 92% in both training and prediction (test) sets. A simple statistical approach was applied to qualitatively correlate the changes in the physicochemical properties (molecular descriptors) of the peptides with the corresponding variations in their biological effects. To illustrate the practical use of the proposed in silico methodology, 12 peptides were designed and predicted by the mtk-chemoinformatic model. Encouraging results were obtained, indicating that these peptides can be considered for future experiments focused on the assessment of anticancer activity and cytotoxicity.

The importance of epoxides as synthetic intermediates in a number of highly added-value chemicals, as well as the search for novel and more sustainable chemical processes have brought considerable attention to the catalytic activity of manganese and iron complexes towards the epoxidation of alkenes using non-toxic terminal oxidants. Particular attention has been given to Mn(salen) and Fe(porphyrin) catalysts. While the former attain remarkable enantioselectivity towards the epoxidation of cis-alkenes, the latter also serve as an important model for the behavior of cytochrome P450, thus allowing the exploration of complex biological processes. In this review, a systematic survey of the bibliographical data for the theoretical studies on Mn- and Fe-catalyzed epoxidations is presented. The most interesting patterns and trends are reported and finally analyzed using an evaluation framework similar to the SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis performed in enterprise media, with the ultimate aim to provide an overview of current trends and areas for future exploration.

Efflux pumps of the ATP-binding cassette transporters superfamily (ABC transporters) are frequently involved in the multidrug-resistance (MDR) phenomenon in cancer cells. Herein, we describe a new atomistic model for the MDR-related ABCG2 efflux pump, also named breast cancer resistance protein (BCRP), based on the recently published crystallographic structure of the ABCG5/G8 heterodimer sterol transporter, a member of the ABCG family involved in cholesterol homeostasis. By means of molecular dynamics simulations and molecular docking, a far-reaching characterization of the ABCG2 homodimer was obtained. The role of important residues and motifs in the structural stability of the transporter was comprehensively studied and was found to be in good agreement with the available experimental data published in literature. Moreover, structural motifs potentially involved in signal transmission were identified, along with two symmetrical drug-binding sites that are herein described for the first time, in a rational attempt to better understand how drug binding and recognition occurs in ABCG2 homodimeric transporters.

This work presents a comprehensive evaluation of the phase behaviour and cohesive enthalpy of protic ionic liquids (PILs) composed of 1,5-diazabicyclo[4.3.0]non-5-ene (DBN) or 1,8-diazabicyclo[5.4.0] undec-7- ene (DBU) organic superbases with short-chain length (acetic, propionic and butyric) carboxylic acids. Glass transition temperatures, T-g, and enthalpies of vaporization, Delta H-vap, were measured for six [BH][A] (1 : 1) PILs (B = DBN, DBU; A = MeCOO, EtCOO, nPrCOO), revealing more significant changes upon increasing the number of -CH2- groups in the base than in the acid. The magnitude of Delta H-vap evidences that liquid PILs have a high proportion of ions, although the results also indicate that in DBN PILs the concentration of neutral species is not negligible. In the gas phase, these PILs exist as a distribution of ion pairs and isolated neutral species, with speciation being dependent on the temperature and pressure conditions at high temperatures and low pressures the separated neutral species dominate. The higher T-g and Delta H-vap of the DBU PILs are explained by the stronger basicity of DBU (as supported by NMR and computational calculations), which increases the extent of proton exchange and the ionic character of the corresponding PILs, resulting in stronger intermolecular interactions in condensed phases.

DFT based calculations were employed in the study of the dissociation of the water molecule onto copper and nickel (110) and (111) surface models, incorporating two additional metallic elements, because it was found previously that metal alloying leads to strong synergic effects in the catalysis of this reaction. The dissociation reaction was studied on the Pt/Ru/Ni, Pt/Ru/Cu, Rh/Ru/Cu, Ni/Ru/Cu and Al/Zn/Cu combinations, in a total of 25 trimetallic surfaces. Very low activation energy barriers for the dissociation of water were calculated on several of the surface models, suggesting that multimetallic surfaces can be interesting alternatives for catalyzing the dissociation of the water molecule, which is a crucial elementary step in the water gas shift reaction. Encouragingly, the calculations predict a facile dissociation of the water molecule onto the (AlZn) Cu(111) catalyst model which is in agreement with recent experimental studies where it was found that a Cu0.5Zn0.5Al2O4 spinel oxide catalyst holds improved activity for the water gas shift reaction.

Due to the great interest that biochemical sensors constructed from graphene nanostructures have raised recently, in this work we analyse in detail the electronic factors responsible for the large affinity of biomolecular units for graphene surfaces using ab initio quantum chemical tools based on density functional theory. Both finite and periodic graphene structures have been employed in our study. Whereas the former allows the analysis of the different energy components contributing to the interaction energy separately, the periodic structure provides a more realistic calculation of the total adsorption energy in an extended graphene surface and serves to validate the results obtained using the finite model. In addition, qualitative relations between interaction energy and electron polarization upon adsorption have been established using the finite model. In this work, we have analysed thermodynamically stable adsorption complexes formed by glycine, melamine, pyronin cation, porphine, tetrabenzoporphine and phthalocyanine with a 2D structure of ninety six carbons and periodic structures formed by cells of fifty and seventy two carbons. Differences in the electrostatic, Pauli repulsion, induction and dispersion energies among aromatic and non-aromatic molecules, charged and non-charged molecules and H-p and stacking interactions have been thoroughly analysed in this work.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural-and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.