Search results for "Biological system"
showing 10 items of 319 documents
Multi-target QSPR assemble of a Complex Network for the distribution of chemicals to biphasic systems and biological tissues
2008
Abstract Chemometrics, that based prediction on the probability of chemical distribution to different systems, is highly important for physicochemical, environmental, and life sciences. However, the amount of information is huge and difficult to analyze. A multi-system partition Complex Network (MSP-CN) may be very useful in this sense. We define MSP-CNs as large graphs composed by nodes (chemicals) interconnected by arcs if a pair of chemicals have similar partition in a given system. Experimental quantification of partition in many systems is expensive, so we can use a Quantitative Structure–Partition Relationship (QSPR) model. Unfortunately, with classic QSPR we need to use one model for…
A novel approach to predict aquatic toxicity from molecular structure
2008
The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity using atom-based non-stochastic and stochastic linear indices. The used dataset consist of 392 benzene derivatives, separated into training and test sets, for which toxicity data to the ciliate Tetrahymena pyriformis were available. Using multiple linear regression, two statistically significant QSAR models were obtained with non-stochastic (R2=0.791 and s=0.344) and stochastic (R2=0.799 and s=0.343) linear indices. A leave-one-out (LOO) cross-validation procedure was carried out achieving values of q2=0.781 (scv=0.348) and q2=0.786 (scv=0.350), respecti…
Prediction of ionic liquid's heat capacity by means of their in silico principal properties
2016
The in silico principal properties (PPs) of ionic liquids (ILs), derived by means of the VolSurf+ approach, were used to develop a Partial Least Squares (PLS) model able to find a quantitative correlation among IL descriptors (accounting for both cationic and anionic structural features) and heat capacity values, providing affordable predictions validated by experimental Cp measurements for an external set of ILs. In silico predictions allowed the selection of a limited number of structurally different ILs with similar Cp values, providing the possibility to select an optimal IL according to efficiency, as well as to environmental and economic sustainability. The present general procedure, …
Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species:
2005
Abstract To date, molecular descriptors do not commonly account for important information beyond chemical structure. The present work, attempts to extend, in this sense, the stochastic molecular descriptors (Gonzalez-Diaz, H. et al., J. Mol. Mod. 2002, 8, 237), incorporating information about the specific biphasic partition system, the biological species, and chemical structure inside the molecular descriptors. Consequently, MARCH-INSIDE molecular descriptors may be identified with time-dependent thermodynamic parameters (entropy and mean free energy) of partition process. A classification function was developed to classify data of 423 drugs and up to 14 different partition systems at the s…
New tyrosinase inhibitors selected by atomic linear indices-based classification models.
2005
In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behavior is shown between the theoretical and experimental results. These results provide a tool that can be used i…
tomocomd-camps and protein bilinear indices - novel bio-macromolecular descriptors for protein research: I. Predicting protein stability effects of a…
2010
Descriptors calculated from a specific representation scheme encode only one part of the chemical information. For this reason, there is a need to construct novel graphical representations of proteins and novel protein descriptors that can provide new information about the structure of proteins. Here, a new set of protein descriptors based on computation of bilinear maps is presented. This novel approach to biomacromolecular design is relevant for QSPR studies on proteins. Protein bilinear indices are calculated from the kth power of nonstochastic and stochastic graph–theoretic electronic-contact matrices, and , respectively. That is to say, the kth nonstochastic and stochastic protein bili…
Atom-Based Quadratic Indices to Predict Aquatic Toxicity of Benzene Derivatives to <i>Tetrahymena pyriformis</i>
2009
The non-stochastic and stochastic atom-based quadratic indices are applied to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity. The used dataset, consisting of 392 benzene derivatives for which toxicity data to the ciliate Tetrahymena pyriformis were available, is divided into training and test sets. The obtained multiple linear regression models are statistically significant (R2 = 0.787 and s = 0.347, R2 = 0.806 and s = 0.329, for non-stochastic and stochastic quadratic indices, respectively) and show rather good stability in a cross-validation experiment (q2 = 0.769 and scv = 0.357, q2 = 0.791 and scv = 0.337, correspondingly). In a…
Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening
2014
Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of …
Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity.
2005
The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classificat…
Prediction of potential environmental toxicity of chemicals in <em>Lactuca sativa</em> seed germination using computational tools
2019
The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of phytotoxicity effects of chemical compounds on the Lactuca sativa seeds germination. A database of 73 compounds, assayed against L. sativa and Dragon’s molecular descriptors are used to obtain a QSAR model for the prediction of the phytotoxicity. The model is carried out with QSARINS software and validated according to OECD principles. The best model showed good value for the determination coefficient (R2 = 0.917) and others parameters appropriate for fitting (s = 0.256 and RMSEtr= 0.236). The validation results confirmed that the model has good robustness and stability …