scholarly journals ASCoVPred: a machine learning-based platform for quantitative prediction of anti-SARS-CoV-2 activity and human cell toxicity of molecules

2021 ◽  
Author(s):  
Arun Sharma ◽  
Neeraj Chaturvedi ◽  
Dinesh Gupta
Keyword(s):  
Machine Learning ◽  
Human Cell ◽  
Molecular Descriptors ◽  
Quantitative Prediction ◽  
Free Access ◽  
Cell Toxicity ◽  
Inhibitory Compounds ◽  
Qsar Models ◽  
Selection For ◽  
Effective Drugs

Abstract There is an urgent need to accelerate the discovery of effective drugs for COVID-19. We have developed machine learning models for rapid discovery of molecules potentially inhibitory to SARS-CoV-2 and negligible or no human cell toxicity. The machine learning (ML) QSAR models were trained and optimized with features (descriptors and fingerprints) of the experimentally validated SARS-CoV-2 inhibitory compounds. Several molecular descriptors and fingerprints were calculated to select the decisive ones for the training and evaluation of thousands of ML models. The best-optimized models are deployed as ASCoVPred webserver and standalone software, that provides easy and free access to the models. The feature selection for selecting the best descriptors for ML models training helped identify a set of decisive descriptors and fingerprints that correlate positively or negatively with the anti-SARS-CoV-2 activity and toxicity of the compounds. Systematic prediction and optimization of compounds with the help of ASCoVPred can facilitate the discovery of novel anti-SARS-CoV-2 compounds. The ASCoVPred web server and standalone software are freely available at http://14.139.62.220/ascovpred/.

scholarly journals Synthesis, diuretic activity research and QSAR-analysis of N-(1,3,4-tiadiazol-2-il)substituted amides of alkanecarboxylic acids

2019 ◽  
pp. 55-65 ◽  
Author(s):  
І. V. Drapak
Keyword(s):  
Molecular Descriptors ◽  
Amide Group ◽  
Diuretic Activity ◽  
Qsar Analysis ◽  
White Rats ◽  
Thiadiazole Ring ◽  
Qsar Models ◽  
Diuretic Agents ◽  
Substituted Alkanes ◽  
Effective Drugs

Diuretics are effective drugs that are widely used in medicine, but have unwanted side effects. The derivative of thiadiazole – acetozolamide is a known diuretic. Therefore, the search for diuretics in this series and the establishment of quantitative «structure–activity» (QSAR) dependencies is appropriate. The aim of the work was to synthesis N-(1,3,4-thiadiazol-2-yl)substituted alkanes of alkanecarboxylic acids, study their diuretic activity, and QSAR analysis. The objects of the study were N-(1,3,4-thiadiazol-2-yl)substituted alkanes of alkanecarboxylic acids, obtained by the interaction of 2-amino-5-alkyl-1,3,4-thiadiazole with the corresponding acylchlorides. Investigation of diuretic activity of synthesized compounds was carried out by the method of Berchin. Hyper-Chem and BuildQSAR software were used for calculation of molecular descriptors and QSAR-models. Synthesis of 12 N-(1,3,4-thiadiazol-2-yl)substituted amides of alkanecarboxylic acids, the structure of which was confirmed by PMR spectroscopy and elemental analysis. Studies of diuretic activity showed that the synthesized compounds had pronounced diuretic properties, and some of them according to activity indicators were approaching or exceeding comparative preparations. Compound N-(5-methyl-[1,3,4]thiadiazol-2-yl) propionamide showed the best diuretic effect: increased daily diuresis in white rats, in comparison with intact control, in 2.47 times (p ≤ 0,001), in comparison with hydrochlorothiazide was in 1,6 times and acetazolamide was 1,75 times. The calculation of the molecular descriptors of N-(1,3,4-thiadiazol-2-yl)substituted amides of alkanecarboxylic acids was conducted. Based on the calculated values of molecular descriptors and diuretic activity values of 12 synthesized compounds, a QSAR analysis was performed. Analysis of structure-diuretic activity showed the greatest influence of lipophilicity, energy parameters, spatial structure and size of the molecule. Moreover, diuretic activity increases with increasing logP, decreasing the refractive, volume and area of the molecule, increasing the energy of the higher occupied molecular orbital. Increasing the charge on the Sulfur atom of the thiadiazole ring and the Оxygen atom of the carbonyl group, reducing the angle between the Sulfur atoms, the Nitrogen of the amide group and the Oxygen, and increasing the angle between the Nitrogene atoms of the thiadiazole ring, the Oxygen and the Nitrogen of the amide group, also increases diuretic activity. The results of the diuretic activity of the synthesized compounds N-(1,3,4-thiadiazol-2-yl)substituted amides of alkanecarboxylic acids show the potential for the search for diuretic agents among 1,3,4-thiadiazole derivatives. The resulting QSAR models will be used to modelling and prediction the activity of new potential diuretics.

A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

2020 ◽  
Vol 26 (26) ◽  
pp. 3049-3058
Author(s):  
Ting Liu ◽  
Hua Tang
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Future Development ◽  
Malaria Parasite ◽  
Mitochondrial Proteins ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Effective Drugs ◽  
Construction Strategies ◽  
Day By Day

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

Molecular Topology and Other Promiscuity Determinants as Predictors of Therapeutic Class - A Theoretical Framework to Guide Drug Repositioning?

2018 ◽  
Vol 18 (13) ◽  
pp. 1110-1122 ◽  
Author(s):  
Juan F. Morales ◽  
Lucas N. Alberca ◽  
Sara Chuguransky ◽  
Mauricio E. Di Ianni ◽  
Alan Talevi ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Topological Descriptors ◽  
Log P ◽  
Acidity Constant ◽  
Molecular Topology ◽  
Clustering Methods ◽  
Mean Values ◽  
Qsar Models

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.

QSAR Analysis of Selected Antimicrobial Structures Belonging to Nitro-derivatives of Heterocyclic Compounds

2020 ◽  
Vol 17 (2) ◽  
pp. 214-225 ◽  
Author(s):  
Piotr Kawczak ◽  
Leszek Bober ◽  
Tomasz Bączek
Keyword(s):  
Antimicrobial Activity ◽  
Heterocyclic Compounds ◽  
Molecular Descriptors ◽  
Microbiological Activity ◽  
Activity Data ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Nitro Derivatives ◽  
Semi Empirical ◽  
Derivatives Of

Background: Nitro-derivatives of heterocyclic compounds were used as active agents against pathogenic microorganisms. A set of 4- and 5-nitroimidazole derivatives exhibiting antimicrobial activity was analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. The study included compounds used both in documented treatment and those described as experimental. Objective: The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data. Methods: During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical and ab initio level of in silico molecular modeling was performed for calculations of molecular descriptors. Results: QSAR models were proposed based on chosen descriptors. The relationship between the nitro-derivatives structure and microbiological activity data was able to class and describe the antimicrobial activity with the use of statistically significant molecular descriptors. Conclusion: The applied chemometric approaches revealed the influential features of the tested structures responsible for the antimicrobial activity of studied nitro-derivatives.

Prediction of Oral Acute Toxicity of Organophosphates Using QSAR Methods

2019 ◽  
Vol 16 ◽  
Author(s):  
Mina Kianpour ◽  
Esmat Mohammadinasab ◽  
Tahereh Momeni Esfahani
Keyword(s):  
Acute Toxicity ◽  
Molecular Descriptors ◽  
Back Propagation ◽  
Quantitative Structure Activity Relationship ◽  
Average Deviation ◽  
Qsar Models ◽  
Ann Models ◽  
Similar Class ◽  
New Compounds ◽  
Oral Acute Toxicity

: The aim of the present study was to develop quantitative structure-activity relationship (QSAR) models, based on molecular descriptors to predict the oral acute toxicity (LD50) of organophosphate compounds. The QSAR models based on genetic algorithm-multiple linear regression (GA-MLR) and back-propagation artificial neural network (BP-ANN) methods were proposed. The prediction experiment showed that the BP-ANN method was a reliable model for screening molecular descriptors, and molecular descriptors obtained by BP-ANN models could well characterize the molecular structure of each compound. It was indicated that among molecular descriptors to predict the LD50 (mgkg-1) of organophosphates, ALOGP2, RDF030u, RDF065p and GATS5m descriptors have more importance than the other descriptors. Also BP-ANN approach with the values of root mean square error (RMSE= 0.00168), square correlation coefficient (R2= 0.9999) and absolute average deviation (AAD=0.6981631) gave the best outcome, and the model predictions were in good agreement with experimental data. The proposed model may be useful for predicting LD50 (mgkg-1) of new compounds of similar class.

scholarly journals Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 122
Author(s):  
Ruggiero Seccia ◽  
Silvia Romano ◽  
Marco Salvetti ◽  
Andrea Crisanti ◽  
Laura Palagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
High Impact ◽  
Prognostic Models ◽  
Early Prediction ◽  
Disease Course ◽  
Slow Progression ◽  
Relapsing Remitting ◽  
Long Time ◽  
Effective Drugs

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

scholarly journals Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4671
Author(s):  
Danpeng Cheng ◽  
Wuxin Sha ◽  
Linna Wang ◽  
Shun Tang ◽  
Aijun Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Life Prediction ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Cycle Life ◽  
Quantitative Prediction ◽  
Test Accuracy ◽  
Machine Learning Algorithm ◽  
Discharge Data

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

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