J. C. Garro Martinez

Bio: J. C. Garro Martinez is an academic researcher from National University of San Luis. The author has contributed to research in topics: Solvent effects & Test set. The author has an hindex of 3, co-authored 4 publications receiving 24 citations.

Impact assessment of the rational selection of training and test sets on the predictive ability of QSAR models.

Abstract: This study performed an analysis of the influence of the training and test set rational selection on the quality and predictively of the quantitative structure-activity relationship (QSAR) model. The study was carried out on three different datasets of Influenza Neuraminidase (H1N1) inhibitors. The three datasets were divided into training and test sets using three rational selection methods: based on k-means, Kennard-Stone algorithm and Activity and the results were compared with Random selection. Then, a total of 31,490 mathematical models were developed and those models that presented a determination coefficient higher than: r2train > 0.8, r2loo > 0.7, r2test > 0.5 and minimum standard deviation (SD) and minimum root-mean square error (RMS) were selected. The selected models were validated using the internal leave-one-out method and the predictive capacity was evaluated by the external test set. The results indicate that random selection could lead to erroneous results. In return, a rational selection allows for obtaining more reliable conclusions. The QSAR models with major predictive power were found using the k-means algorithm and selection by activity.

A preliminary theoretical study of antiepileptic drugs

Abstract: This work is aimed to investigate the structural characteristics of several open chain enaminones, a group of organic compounds containing the conjugated system NC=C-C=O, with the assumption that they possess two main characteristics: transportability through biological membranes and pharmacological effects as antiepileptics by binding to the voltage-gated sodium ion channel. To explore this possibility, density functional calculations were used to find the minimum energy conformations of nine candidate molecules. The conformational analysis was carried out by comparing the characteristics of the structures based on graphical and superposition techniques.

Solvent effect on the Thermodynamics of the Oxidation of Potential Antioxidant Garlic Compounds

Abstract: Antioxidant capacity of garlic has been attributed to organic sulfur compounds such us allyl methyl disulfide. Using quantum chemical calculations at B3LYP/6-31+G (d) and G3MP2B3/6-31+G (d) levels of theory, we study three possible oxidation reactions of this compound against hydrogen peroxide, a reactive oxygen species, from a thermodynamic point of view. Because these reactions are supposed to occur in biological media, solvent effect was taken into consideration. Oxidation over the double bond that leads to the formation of an epoxide is more thermodynamically feasible, limiting the antioxidant capacity of this compound.

Computer-Aided Linear Modeling Employing Qsar for Drug Discovery

Abstract: Quantitative structure-activity relationship (QSAR) is a computational process that relates the chemical structure of compounds with their activities, especially biologic activities or effects. It employs series of computer-based processes to analyze quantitative experimental data of the activities of given compounds with known chemical structures in order to predict a relationship, model or equation that will help to propose the activity of known compounds with unknown activities or unknown compounds and their activities. Commonly used computer softwares in QSAR analysis include HYPERCHEM, MATLAB, DRAGON and RECKON. Key words: QSAR, biological activity, prediction, computer software.

QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides

Abstract: The assessment of the environmental fate and (eco)toxicological effects of pesticide compounds is of crucial importance. The present review is focused on Quantitative Structure-Property Relationships (QSPR) applications on three environmentally relevant physicochemical properties of pesticides, which can be used for assessing their environmental partition and transport, as well as exposure potential namely water solubility, octanol-water partition coefficient and vapour pressure. This article revises various interesting QSPR applications with special emphasis on studies developed during the 2009-2019 period.

QSPR study of the Henry’s law constant for heterogeneous compounds

Abstract: We establish a Quantitative Structure-Property Relationships study on the Henry’s law constant of 530 heterogeneous compounds, including pesticides, solvents, aromatic hydrocarbons and persistent pollutants. The multivariable linear regression models are established with the Replacement Method (RM) technique, by searching the best 1–8 molecular descriptors on 26,795 available non-conformational structural variables. These descriptors are derived from different freely available softwares, such as PaDEL, Mold2, DataWarrior, QuBiLs-MAS and CORAL. The present results are compared with the estimations provided by the HENRYWIN module of EPI Suite, and serve as a tool for predicting the Henry’s law constant on related chemical structures.