Immunotoxicity, Flow Cytometry, and Chemoinformatics: Review, Bibliometric Analysis, and New QSAR Model of Drug Effects Over Macrophages

Tenorio-Borroto, Esvieta; Penuelas-Rivas, Claudia G.; Vasquez-Chagoyan, Juan C.; Prado-Pradoa, Francisco J.; Garcı́a-Mera, Xerardo; González-Dı́az, Humberto

doi:10.2174/1568026611209061815

Cited by 6 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model classified the data set obtained from multiplexing assays with an accuracy of 92%. Follow-up investigations have been published in which various descriptors were used to generate new multitask QSAR-ANN models with the aim of predicting the immunotoxicity of chemicals. − The first work devoted to the discovery of safe antibacterial drugs by this approach was reported by Speck-Planche et al This model achieved high prediction accuracies for both the training and prediction sets. By following the same methodology and conceptual process, several promising multitask QSAR models have been constructed. ,− …”

Section: Artificial Intelligence Approaches In Ligand-based Virtual S...mentioning

confidence: 99%

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

View full text Add to dashboard Cite

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

show abstract

Section: Artificial Intelligence Approaches In Ligand-based Virtual S...mentioning

confidence: 99%

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In other work [13], we have developed one mt-QSTR model based on the spectral moments of on stochastic matrix. The model with only four variables is able to assign each drug to 1 out of 2 possible activity classes: active (C = 1) or non-active compounds (C = 0), given the molecular structure and several multiplex assay conditions m j .…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Model for high-throughput screening of drug immunotoxicity – Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry

Tenorio-Borroto

Penuelas-Rivas²,

Vasquez-Chagoyan³

et al. 2014

European Journal of Medicinal Chemistry

Self Cite

View full text Add to dashboard Cite

Quantitative Structure-Activity (mt-QSAR) techniques may become an important tool for prediction of cytotoxicity and High-throughput Screening (HTS) of drugs to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and Linear Discriminant Analysis (LDA) function. This model correctly classifies 8258 out of 9000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 μg/mL and Cytotoxicity = 23.6% for the anti-microbial/anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). The results obtained complement the toxicological studies of this important drug. This work adds a new tool to the existing pool of few methods useful for multi-target HTS of ChEMBL and other libraries of compounds towards drug discovery.

show abstract

CORAL: Building up QSAR models for the chromosome aberration test

Toropov

Toropova

Raitano

et al. 2019

Saudi Journal of Biological Sciences

View full text Add to dashboard Cite

A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of “CORrelation And Logic”) is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure–Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.

show abstract

Immunotoxicity, Flow Cytometry, and Chemoinformatics: Review, Bibliometric Analysis, and New QSAR Model of Drug Effects Over Macrophages

Cited by 6 publications

References 0 publications

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Model for high-throughput screening of drug immunotoxicity – Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry

CORAL: Building up QSAR models for the chromosome aberration test

Contact Info

Product

Resources

About