QSAR study on binding affinity of PATs (rodenticides) to the [3H]-mepyramine-labelled H1 receptor in rat and guinea pig brain

3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.

Modelling of carbonic anhydrase inhibitory activity of sulfonamides using molecular negentropy

The present paper deals with the modelling of carbonic anhydrase inhibitory activity of sulfonamides using molecular negentropy (N). Excellent results are obtained in multiple regression analysis upon introduction of indicator parameters. The results are critically discussed on the basis of statistical data obtained from regression analysis.

On the topological evidences for modelling lipophilicity

Topological evidences for modelling lipophilicity of a large series of diversed compounds have been provided on the basis of distance-based topological indices. A pool of topological indices along with indicator parameters related to the type of the compounds present in the set of 140 compounds were used for this purpose. The results have shown that topology as well as the type of compounds are the responsible parameters for modelling lipophilicity.

Quantitative structure-activity relationship studies on 5-phenyl-3-ureido-1,5-benzodiazepine as cholecystokinin-A receptor antagonists

Quantitative structure-activity relationship (QSAR) studies on a series of 5-phenyl-3-ureido-1,5-benzodiazepine-2,4-diones has been carried out using a pool of distance-based topological indices. Step-wise regression analysis indicated that penta-parametric regression expression containing Sz, B, Ip1, Ip2 and Ip3 is the most potent and selective for CCK-A affinity. The predictive potential of the model is discussed on the basis of cross-validation parameters as well as by estimating root mean square (RMSR) of the residuals.

QSAR study on competition binding of rodenticides (PATs) to H(1) receptor in rat and guinea pig brain

A quantitative structure-activity relationship (QSAR) study on binding activity for a series of rodenticides (PAT analogues) to the [(3)H]-mepyramine-labeled H(1) receptor in rat and guinea pig brain is attempted topologically. From the pool of molecular descriptors we, observed that the most significant descriptor is the molecular redundancy index (MRI). Multiple regression analysis gave excellent results with MRI upon introduction of some dummy parameters (indicator parameters). Predictive ability of the proposed models is discussed using cross-validation parameters.