A computational study of clusters containing azulene and up to two molecules of water or hydrogen sulfide was carried out to elucidate the main characteristics of these X-H···π interacting systems. For clusters with one H₂X molecule only one structure was found interacting with the aromatic cloud of azulene, with an interaction energy of -3.1 kcal/mol both for H₂O and H₂S as calculated at the CCSD(T)/AVDZ level. On the other hand, MP2 overestimates the interaction in hydrogen sulfide clusters, whereas the MPWB1K functional produces values in very good agreement with CCSD(T). A variety of structures were located for clusters with two H₂X molecules. The most stable ones are those which simultaneously present hydrogen bond between H₂X molecules and X-H···π contacts. Also, only this kind of structure presents relevant three body stabilizing contributions. On the other hand, the interaction of azulene with (H₂X)₂ dimer is stronger precisely for structures which do not present X-H···X hydrogen bond. This suggest that for larger systems, structures with the molecules distributed over the aromatic surface but without interacting among them, can be competitive with other, hydrogen bonded clusters, especially in H₂S containing systems.

The intramolecular [6+2] cycloaddition mechanism of 2­vinylcyclobutanones and  alkenes catalyzed by the [Rh(CO)₂Cl]₂ dimer has been studied using density functional theory,  comparing this multistep process with the one­step reaction in absence of catalyst. This possible  mechanism agrees with what was previously experimentally suggested. Calculations have also  allowed to explain the selectivity of the reaction.

Valence topological charge-transfer (CT) indices are applied to the calculation of pH at the pI isoelectric point. The combination of CT indices allows the estimation of pI. The model is generalized for molecules with heteroatoms. The ability of the indices for the description of molecular charge distribution is established by comparing them with the pI of 21 amino acids. Linear correlation models are obtained. The CT indices improve multivariable regression equations for pI. The variance decreases by 95%. No superposition of the corresponding G_k–J_k and G_k^V–J_k^V pairs is observed in most fits, which diminishes the risk of collinearity. The inclusion of heteroatoms in π-electron system is beneficial for the description of pI, owing to either the role of the additional p orbitals provided by heteroatom or role of steric factors in π-electron conjugation. The use of only CT and valence CT indices {G_k,J_k,G_k^V,J_k^V} gives limited results for modelling pI of amino acids. Furthermore, the inclusion of the numbers of acidic and basic groups improves all models. The effect is specially noticeable for amino acids with more than two functional groups. The fitting line obtained for the 21 amino acids can be used to estimate the isoelectric point of lysozyme and its fragments, by only replacing (1+Δn/n_T) with (M+Δn)/n_T. For lysozyme, the results of smaller fragments can estimate that of the whole protein with 1–13% errors.

Blood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP identifying general Pro-EDICToRs patterns instead of a single protein marker may represents a more realistic alternative. In this sense, first we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we introduced the graph node-overlapping parameters (nop_k) to numerically characterize SP-MS using them as inputs to seek a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. Principal Component Analysis (PCA) on the nop_k values present in the QPTR model explains with one factor (F₁) the 82.7% of variance. Next, these nop_k values were used to construct by the first time a Pro-EDICToRs Complex Network having nodes (samples) linked by edges (similarity between two samples). We compared the topology of two sub-networks (cardiac toxicity and control samples); finding extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared subnetworks with well known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F₁-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results shows that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome research.

There are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model, which outputs were the inputs of the above-mentioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extent model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power-law network with few mechanisms (network hubs).