Importance of Applicability Domain of QSAR Models

Applicability Domain ◽

Property Prediction ◽

Qsar Models ◽

Environmental Fate Modeling

Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.

A QSAR STUDY OF SUBSTITUTED PYRAZOLINE DERIVATIVES AS POTENTIAL ANTI-TUBERCULOSIS AGENTS

INDIAN DRUGS ◽

10.53879/id.54.04.10781 ◽

2017 ◽

Vol 54 (04) ◽

pp. 22-31

Author(s):

M. C Sharma ◽

Keyword(s):

Statistical Significance ◽

External Validation ◽

Model Development ◽

Qsar Model ◽

Partial Least Square ◽

Least Square ◽

Qsar Study ◽

Partial Least Square Analysis ◽

A quantitative structure–activity relationship (QSAR) of a series of substituted pyrazoline derivatives, in regard to their anti-tuberculosis activity, has been studied using the partial least square (PLS) analysis method. QSAR model development of 64 pyrazoline derivatives was carried out to predict anti-tubercular activity. Partial least square analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.7426 and 0.7903, respectively. Two-dimensional QSAR analyses of such pyrazoline derivatives provide important structural insights for designing potent antituberculosis drugs.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200902141907 ◽

2020 ◽

Vol 23 ◽

Author(s):

Apilak Worachartcheewan ◽

Alla P. Toropova ◽

Andrey A. Toropov ◽

Reny Pratiwi ◽

Virapong Prachayasittikul ◽

...

Keyword(s):

Histone Deacetylases ◽

Rational Design ◽

Qsar Model ◽

Sirtuin 1 ◽

Data Set ◽

Functional Roles ◽

Molecular Features ◽

Oecd Principles ◽

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

An In Silico Model for Predicting Drug-Induced Hepatotoxicity

International Journal of Molecular Sciences ◽

10.3390/ijms20081897 ◽

2019 ◽

Vol 20 (8) ◽

pp. 1897 ◽

Cited By ~ 13

Author(s):

Shuaibing He ◽

Tianyuan Ye ◽

Ruiying Wang ◽

Chenyang Zhang ◽

Xuelian Zhang ◽

...

Keyword(s):

Large Scale ◽

Characteristic Curve ◽

External Validation ◽

Model Development ◽

Qsar Model ◽

New Drugs ◽

Drug Induced ◽

Drug Candidates ◽

External Test

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

A QSAR Modeling on Aurone Derivatives as Antimalarial Agents

Asian Journal of Chemistry ◽

10.14233/ajchem.2020.22846 ◽

2020 ◽

Vol 32 (11) ◽

pp. 2839-2845

Author(s):

R. Hadanau

Keyword(s):

Qsar Model ◽

Multilinear Regression ◽

Qsar Modeling ◽

Qsar Analysis ◽

Antimalarial Agents ◽

Inhibition Concentration ◽

Qsar Models ◽

Semi Empirical ◽

Statistical Criteria

A quantitative structure activity relationship (QSAR) analysis was performed on several compound and aurone derivatives (1-16) and 17-21 compounds were used as internal and external tests, respectively. Studies have investigated aurone derivatives; however, for aurone compounds, QSAR analysis has not been conducted. The semi-empirical PM3 method of HyperChem for Windows 8.0 was used to optimise the aurone derivative structures to acquire descriptors. For 15 influential descriptors, the multilinear regression MLR analysis was conducted by employing the backward method, and four new QSAR models were obtained. According to statistical criteria, model 2 was the optimum QSAR model for predicting the inhibition concentration (IC50) theoretical value against novel aurone derivatives. The modelling of 40 (22-61) aurone compounds was achieved. Six novel compounds (54, 55, 58, 59, 60, and 61) were synthesized in a laboratory because the IC50 of these compounds was lower than that of chloroquine (IC50 = 0.14 μM).

An In Silico QSAR Model Study Using Electrophilicity as a Possible Descriptor Against T. Brucei

International Journal of Chemoinformatics and Chemical Engineering ◽

10.4018/ijcce.20190701.oa1 ◽

2019 ◽

Vol 8 (2) ◽

pp. 57-68 ◽

Cited By ~ 1

Author(s):

Ranita Pal ◽

Goutam Pal ◽

Gourhari Jana ◽

Pratim Kumar Chattaraj

Keyword(s):

Qsar Model ◽

Disease Spread ◽

Tsetse Flies ◽

Electrophilicity Index ◽

Data Set ◽

Model Study ◽

Sum Of Ranking Differences ◽

Qsar Models ◽

Vector Borne

Human African trypanosomiasis (HAT) is a vector-borne sleeping sickness parasitic disease spread through the bite of infected tsetse flies (Glossina genus), which is highly populated in rural Africa. The present study constructed quantitative structure-activity relationship (QSAR) models based on quantum chemical electronic descriptors to bring out the extent to which the electronic factor of the selected compounds affects the HAT activity. Theoretical prediction of toxicity (pIC50) of the series of heterocyclic scaffolds consisting 32 pyridyl benzamide derivatives towards HAT is investigated by considering all possible combinations of electrophilicity index (ω) and the square of electrophilicity index (ω2) as descriptors in the studied models along with other descriptors previously used by Masand et al. A multiple linear regression (MLR) analysis is conducted to develop the models. Further, in order to obtain the variable selection on the overall data set having diverse functional groups, the analysis using sum of ranking differences methodology with ties is carried out.

Synthesis, Cytotoxic Activity Evaluation and Quantitative Structure-ActivityAnalysis of Substituted 5,8-Dihydroxy-1,4-naphthoquinones and Their O- and S-Glycoside Derivatives Tested against Neuro-2a Cancer Cells

Marine Drugs ◽

10.3390/md18120602 ◽

2020 ◽

Vol 18 (12) ◽

pp. 602

Author(s):

Sergey Polonik ◽

Galina Likhatskaya ◽

Yuri Sabutski ◽

Dmitry Pelageev ◽

Vladimir Denisenko ◽

...

Keyword(s):

Cytotoxic Activity ◽

Sea Urchins ◽

Neuroblastoma Cells ◽

Qsar Model ◽

Quantitative Structure ◽

Mtt Method ◽

Structure Activity ◽

Cytotoxic Compounds ◽

Based on 6,7-substituted 2,5,8-trihydroxy-1,4-naphtoquinones (1,4-NQs) derived from sea urchins, five new acetyl-O-glucosides of NQs were prepared. A new method of conjugation of per-O-acetylated 1-mercaptosaccharides with 2-hydroxy-1,4-NQs through a methylene spacer was developed. Methylation of 2-hydroxy group of quinone core of acetylthiomethylglycosides by diazomethane and deacetylation of sugar moiety led to 28 new thiomethylglycosidesof 2-hydroxy- and 2-methoxy-1,4-NQs. The cytotoxic activity of starting 1,4-NQs (13 compounds) and their O- and S-glycoside derivatives (37 compounds) was determined by the MTT method against Neuro-2a mouse neuroblastoma cells. Cytotoxic compounds with EC50 = 2.7–87.0 μM and nontoxic compounds with EC50 > 100 μM were found. Acetylated O- and S-glycosides 1,4-NQs were the most potent, with EC50 = 2.7–16.4 μM. Methylation of the 2-OH group innaphthoquinone core led to a sharp increase in the cytotoxic activity of acetylated thioglycosidesof NQs, which was partially retained for their deacetylated derivatives. Thiomethylglycosides of 2-hydroxy-1,4-NQs with OH and MeO groups in quinone core at positions 6 and 7, resprectively formed a nontoxic set of compounds with EC50 > 100 μM. A quantitative structure-activity relationship (QSAR) model of cytotoxic activity of 22 1,4-NQ derivatives was constructed and tested. Descriptors related to the cytotoxic activity of new 1,4-NQ derivatives were determined. The QSAR model is good at predicting the activity of 1,4-NQ derivatives which are unused for QSAR models and nontoxic derivatives.

Estimation ofn-Octanol/Water Partition Coefficients (log KOW) of Polychlorinated Biphenyls by Using Quantum Chemical Descriptors and Partial Least Squares

Journal of Chemistry ◽

10.1155/2013/740548 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Fang-Li Zhang ◽

Xing-Jian Yang ◽

Xiu-Ling Xue ◽

Xue-Qin Tao ◽

Gui-Ning Lu ◽

...

Keyword(s):

Polychlorinated Biphenyls ◽

Least Squares ◽

Partial Least Squares ◽

Density Functional ◽

Environmental Fate ◽

Qsar Model ◽

Quantum Chemical Descriptors ◽

Water Partition Coefficient ◽

Predicted Values

Then-octanol/water partition coefficient (log KOW) is a useful parameter for the assessment of the environmental fate and impact of xenobiotic trace contaminants. Quantitative structure-activity relationship (QSAR) model for log KOWof polychlorinated biphenyls (PCBs) was analyzed by using the density functional theory at B3LYP/6-31G(d) level and the partial least squares (PLS) method with an optimizing procedure. A PLS model with reasonably good coefficient (R2=0.992) and cross-validation test (Q2cum=0.988) values was obtained. All the predicted values are within the range of±0.3log unit from the observed values. The log KOWvalues of 7 PCBs in the test set predicted by the model are very close to those observed, indicating that this model has high fitting precision and good predictability. The PLS analysis showed that PCBs with larger electronic spatial extent and lower molecular total energy values tend to be more hydrophobic and lipophilic.

FP2VEC: a new molecular featurizer for learning molecular properties

Bioinformatics ◽

10.1093/bioinformatics/btz307 ◽

2019 ◽

Vol 35 (23) ◽

pp. 4979-4985 ◽

Cited By ~ 8

Author(s):

Woosung Jeon ◽

Dongsup Kim

Keyword(s):

Deep Learning ◽

Language Processing ◽

Chemical Compounds ◽

Qsar Model ◽

Supplementary Information ◽

Learning Technology ◽

Natural Languages ◽

Qsar Models ◽

Benchmark Datasets

Abstract Motivation One of the most successful methods for predicting the properties of chemical compounds is the quantitative structure–activity relationship (QSAR) methods. The prediction accuracy of QSAR models has recently been greatly improved by employing deep learning technology. Especially, newly developed molecular featurizers based on graph convolution operations on molecular graphs significantly outperform the conventional extended connectivity fingerprints (ECFP) feature in both classification and regression tasks, indicating that it is critical to develop more effective new featurizers to fully realize the power of deep learning techniques. Motivated by the fact that there is a clear analogy between chemical compounds and natural languages, this work develops a new molecular featurizer, FP2VEC, which represents a chemical compound as a set of trainable embedding vectors. Results To implement and test our new featurizer, we build a QSAR model using a simple convolutional neural network (CNN) architecture that has been successfully used for natural language processing tasks such as sentence classification task. By testing our new method on several benchmark datasets, we demonstrate that the combination of FP2VEC and CNN model can achieve competitive results in many QSAR tasks, especially in classification tasks. We also demonstrate that the FP2VEC model is especially effective for multitask learning. Availability and implementation FP2VEC is available from https://github.com/wsjeon92/FP2VEC. Supplementary information Supplementary data are available at Bioinformatics online.

QSAR and Structure-Based Docking Studies of Aryl Pyrido[2,3-d]pyrimidin-7(8H)-ones

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2018010103 ◽

2018 ◽

Vol 3 (1) ◽

pp. 43-73 ◽

Cited By ~ 1

Author(s):

Jyoti Durgapal ◽

Neha Bisht ◽

Muneer Alam ◽

Dipiksha Sharma ◽

Mohd Salman ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Model Validation ◽

Biological Activities ◽

Qsar Model ◽

Structural Features ◽

Docking Studies ◽

Linear Regression Method ◽

The target of the present study has been to carry out computer-aided anticancer drug design utilizing genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure activity relationship (QSAR) of fibroblast growth factor (FGFr) inhibition of pyrido[2,3-d]pyrimidine-7(8H)-one compounds utilizing different classes of computed structural descriptors. A QSAR model was developed utilizing a combination of constitutional, functional group, geometrical and atom-centered fragment indices by multiple linear regression method and the model validation was performed by searching the predictability of the QSAR models. After outlier analyses through applicability domain, the model validation results were improved. In this connection, molecular docking studies were performed to predict the mode of binding and important structural features necessary for producing biological activities. This attempt could be helpful for further modeling of potent less toxic anticancer chemotherapeutics in these congeners.