QSAR modelling: a therapeutic patent review 2010-present

Halder, Amit Kumar; Moura, Ana S.; Cordeiro, M. Natália D. S.

doi:10.1080/13543776.2018.1475560

Cited by 27 publications

(12 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The development of these models requires the previous characterization of the molecules using numerical descriptors and the subsequent application of different statistical and ML tools to generate the algorithms that relate these descriptors with the studied parameter. After the development and validation of a QSAR model, it can be employed as a prediction tool for the property/activity of new molecules with known chemical structures [ 44 , 45 ]. Figure 2 illustrates a general workflow for constructing QSAR models, which starts with the curation of the dataset of molecules to be employed.…”

Section: Methodsmentioning

confidence: 99%

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review

Carpio¹,

Sanz

Gozalbes³

et al. 2021

Mol Divers

View full text Add to dashboard Cite

Graphical Abstract Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods.

show abstract

Section: Methodsmentioning

confidence: 99%

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review

Carpio¹,

Sanz

Gozalbes³

et al. 2021

Mol Divers

View full text Add to dashboard Cite

show abstract

“…They are computational methods that attempt creating relationships between chemical structure features of a set of compounds and one of their biological activities expressed numerically [ 15 ]. The practical applications and uses of QSAR span a wide range, from establishing structural requirements for the prospective ligands to finding new prospective compounds via virtual screening and to estimation of ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) features of a large number of chemical compounds [ 16 ]. Valid QSAR models allow virtual screening of large and very large databases of chemical compounds, resulting in identification with meager costs of chemical compounds with a high potential of being active and satisfying the preconditions of promising drugs [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

2021

View full text Add to dashboard Cite

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

show abstract

“…Among these, quantitative structure–activity relationship (QSAR) analysis can predict physiological activity, toxicity, enzymatic reactions, receptor agonist/antagonist activity, environmental fate, etc. (Bloomingdale et al, 2017; Polishchuk, 2017; Halder et al, 2018; Khan and Roy, 2018; Simões et al, 2018). This analysis is conducted based on a formulation of established rules for the relationship between the chemical structure of a compound and its activity and relies on the structural, quantum chemical, and physicochemical features, which are represented as various numerical molecular descriptors (Dougall, 2001; Fang et al, 2003; Roy and Das, 2014; Silva and Trossini, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis

Matsuzaka

Uesawa

2019

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

show abstract

QSAR modelling: a therapeutic patent review 2010-present

Cited by 27 publications

References 35 publications

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis

Contact Info

Product

Resources

About