In silicoAntibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

Castillo-Garit, Juan A.; Marrero-Ponce, Yovani; Barigye, Stephen J.; Medina-Marrero, Ricardo; Bernal, Milagros García; Vega, José Manuel García de la; Torrens, Francisco; Arán, Vicente J.; Pérez-Giménez, Facundo; García-Domènech, Ramón; Acevedo-Barrios, Rosa

doi:10.5935/0103-5053.20150087

Cited by 8 publications

(7 citation statements)

References 2 publications

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“…Furthermore, the predictive power of many published models was not verified by cross-validation or by using an external validation set of fairly diverse compounds (Garcia-Domenech and de Julian-Ortiz, 1998; Tomas-Vert et al, 2000; Aptula et al, 2003; Murcia-Soler et al, 2004; Gonzalez-Diaz et al, 2005). Nevertheless, only a small part of these models was employed in a routine virtual screening practice (Marrero-Ponce et al, 2005; Wang et al, 2014; Castillo-Garit et al, 2015; Masalha et al, 2018) and resulted in the discovery of novel hit compounds with a remarkable antibacterial activity (Gonzalez-Diaz et al, 2005; Wang et al, 2014; Masalha et al, 2018). In 2015, Castillo-Garit and co-workers performed a ligand-based virtual screening study of 116 molecules with reported antibacterial activity using the developed QSAR model (Castillo-Garit et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, only a small part of these models was employed in a routine virtual screening practice (Marrero-Ponce et al, 2005; Wang et al, 2014; Castillo-Garit et al, 2015; Masalha et al, 2018) and resulted in the discovery of novel hit compounds with a remarkable antibacterial activity (Gonzalez-Diaz et al, 2005; Wang et al, 2014; Masalha et al, 2018). In 2015, Castillo-Garit and co-workers performed a ligand-based virtual screening study of 116 molecules with reported antibacterial activity using the developed QSAR model (Castillo-Garit et al, 2015). The model demonstrated good predictive ability in differentiation between active and inactive molecules.…”

Section: Introductionmentioning

confidence: 99%

“…For a long time, linear discriminant analysis (LDA) (Garcia-Domenech and de Julian-Ortiz, 1998; Mishra et al, 2001; Cronin et al, 2002; Aptula et al, 2003; Molina et al, 2004; Murcia-Soler et al, 2004; Gonzalez-Diaz et al, 2005; Marrero-Ponce et al, 2005; Karakoc et al, 2006; Castillo-Garit et al, 2015) and ANN (Garcia-Domenech and de Julian-Ortiz, 1998; Tomas-Vert et al, 2000; Murcia-Soler et al, 2004; Cherkasov, 2005; Karakoc et al, 2006) were the most popular machine learning methods that were used for prediction of antibacterial activity. On the contrary, few studies successfully implemented other in silico techniques, for example, binary logistic regression (BLR) (Cronin et al, 2002; Aptula et al, 2003), SVM (Yang et al, 2009; Wang et al, 2014), kNN (Karakoc et al, 2006; Yang et al, 2009; Wang et al, 2014), and decision tree (DT) (Yang et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Novel Antibacterials Using Machine Learning Techniques

et al. 2019

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Many pharmaceutical companies are avoiding the development of novel antibacterials due to a range of rational reasons and the high risk of failure. However, there is an urgent need for novel antibiotics especially against resistant bacterial strains. Available in silico models suffer from many drawbacks and, therefore, are not applicable for scoring novel molecules with high structural diversity by their antibacterial potency. Considering this, the overall aim of this study was to develop an efficient in silico model able to find compounds that have plenty of chances to exhibit antibacterial activity. Based on a proprietary screening campaign, we have accumulated a representative dataset of more than 140,000 molecules with antibacterial activity against Escherichia coli assessed in the same assay and under the same conditions. This intriguing set has no analogue in the scientific literature. We applied six in silico techniques to mine these data. For external validation, we used 5,000 compounds with low similarity towards training samples. The antibacterial activity of the selected molecules against E. coli was assessed using a comprehensive biological study. Kohonen-based nonlinear mapping was used for the first time and provided the best predictive power (av. 75.5%). Several compounds showed an outstanding antibacterial potency and were identified as translation machinery inhibitors in vitro and in vivo . For the best compounds, MIC and CC 50 values were determined to allow us to estimate a selectivity index (SI). Many active compounds have a robust IP position.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Novel Antibacterials Using Machine Learning Techniques

et al. 2019

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“…"In silico" methods are useful tools for screening chemicals, especially in early stages of the drug discovery process [5][6][7][8]. In the last two decades these studies have played a fundamental role in the development of a number of drugs that are currently on the market [9].…”

Section: Introductionmentioning

confidence: 99%

An approach toward the identification of new antileishmaniasic compounds.

Castillo-Garit¹,

Flores-Balmaseda²,

Álvarez³

2017

Proceedings of MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd Edition

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Herein we present results of a quantitative structure-activity relationship (QSAR) study to identify new antileishmaniasic compounds (Leishmania amazonensis) by using a set of more than 2000 0D-2D Dragon´s molecular descriptors and machine learning techniques. A data set of organic chemicals, with antileishmaniasic activity against promastigote forms of the parasite, is used to develop four QSAR models based on k-nearest neighbors, Support Vector Machine, Multi-Layer Perceptron and classification tree techniques. External validation procedures were developed to demonstrate the predictive power of the models. Promastigote´s models correctly classify more than 89% chemicals in both training and external prediction groups, respectively. In addition to the individual techniques an assembled system of majority vote was personalized with the aim of improving the results of the obtained models. To identify new compounds with potential activity against this parasite, a virtual screening was performed using DrugBank international database. There were identified more than five hundred new potential antileishmaniasic compounds. The current results constitute a step forward in the search for efficient ways to discover new antileishmaniasic lead compounds.

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“…In light of this virtual (computational) screening of chemical libraries has emerged as a complementary approach to techniques using the classical -trial and error-screenings [5][6]. By this means, computational techniques are used to select a reduced number of potentially active compounds from large available chemical or combinatorial libraries [7][8][9]. This in silico procedure will be used here in order to find predictive models that permit us the -rationalselection/identification as well as the design of new analgesics with the required properties.…”

Section: Introductionmentioning

confidence: 99%

QSAR Models and Virtual Screening for Discovery of New Analgesic Leads

Castillo-Garit

López²,

Marrero-Ponce

et al. 2017

Proceedings of MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd Edition

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The search for new selective pharmacological agents with no significant side-effects is an increasing requirement for the development of new drugs to be used in the treatment of acute and chronic pain. In the present study, a new series of compounds (VAM 1,6,10,11, 12, 24) has been screening in QSARLDA mathematic models and pharmacologically evaluated. The antinociceptive properties of the new analgesic candidates obtained of virtual screening have been investigated in vitro tests. The pre-treatment with the compounds VAM 1, 2-4, 6, 10, 11, 12, showed a potent inhibition of IL-6 on RAW cells. The blocking efficacy of nineteen compounds on several isoforms of voltage-dependent sodium channels, expressed in Xenopus laevis oocytes, was tested (Nav1.3, Nav1.5, Nav1.6, Nav1.7, and Nav1.8). An exception was Nav1.6, where VAM 24 compound to result in substantial block indicating that acts specifically at this peculiar isoform. Compounds VAM 10 and VAM 2-4 are the most potent antinociceptive agents. These results indicate the potential of the compound VAM 2-4 to treat pain conditions.

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In silicoAntibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

Cited by 8 publications

References 2 publications

Identification of Novel Antibacterials Using Machine Learning Techniques

Identification of Novel Antibacterials Using Machine Learning Techniques

<strong>An approach toward the identification of new antileishmaniasic compounds.</strong>

<strong>QSAR Models and Virtual Screening for Discovery of New Analgesic Leads</strong>

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