External Predictive Ability Research Articles

Balanced QSAR analysis to identify the structural requirements of ABBV-075 (Mivebresib) analogues as bromodomain and extraterminal domain (BET) family bromodomain inhibitor

ABBV-075 (Mivebresib) analogues were reported to have BET family bromodomain binding affinity Ki in nano-molar (nM) range (0.8 to 3000 nM). But future optimizations are required to achieve a drug-like molecule with retention of high binding affinity and optimum ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) profile. This could be achieved by identifying the pharmacophoric features (salient and concealed) using modern techniques like CADD (Computer-Aided Drug Designing). Therefore, in the present work, QSAR (Quantitative structure-activity relationship) analysis, a thriving CADD branch, has been executed to achieve the determined objectives. The developed QSAR model is statistically acceptable with robust fitting, high internal and external predictive ability. The developed model fulfils the threshold values for a good number of statistical parameters like R2 = 0.80, R2CV = 0.77, etc. The analysis reveals that non-ring Carbon/Nitrogen atoms, frequency of occurrence of specific combinations of Carbon/Nitrogen atoms with acceptor/donor atoms are important pharmacophoric features for BET binding affinity. Thus, the developed QSAR has a balance of quantitative and qualitative approaches. The results could be useful for future optimizations of Mivebresib analogues .

Quantitative structure-property relationship of standard enthalpies of nitrogen oxides based on a MSR and LS-SVR algorithm predictions

Quantum chemistry method is used to calculate 1444 descriptors of each nitrogen oxide molecule. Variables are screened by using multiple stepwise regression (MSR). The optimal ten-element regression equation is derived, its R2 = 0.934 and Q2 = 0.927. The support vector regression (SVR) model between ten molecular descriptors and standard formation enthalpy is established, and the support vector machine is optimized by using the least squares method (LS). The training set consisting of 78 compounds have R2 = 0.969 and Q2 = 0.954. The other 22 compounds constitute the test set to verify the external prediction ability of the model, and its R2 = 0.958. It demonstrates that the LS-SVR model has good stability and prediction ability, and overcomes the problem of over-fitting. The conclusion of quantitative structural property relationship (QSPR) shows that the molecular descriptor and the standard enthalpy of formation are non-linear second-order functions. We speculate that the electronegativity of atoms is the key to determine the standard enthalpy of formation of compounds

Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in monte carlo optimization.

Drug abuse is a worldwide wide problem affecting individual, society and the environment in general and it is nothing less than the attempted ecocide. Designer drugs are the chemical substances used for recreational purposes and have addictive properties. The production of designer drugs at disturbing pace is creating difficulties for the investigators in their testing. Computational evaluation method can be an interesting approach for early checking of abusive drugs. In the present work, quantitative structure activity relationship (QSAR) models are developed for abusive potential of designer drugs using SMILES and graph based parameters. Dopamine transporter/serotonin transporter inhibition (DAT/SERT) ratio was used as endpoint and the whole data set was divided into eight non identical splits for development of the models using balance of correlation technique of Monte Carlo optimization. The internal and external cross validation results confirmed that the models created with index of ideality of correlation were reliable and robust in prediction. The developed models followed all the five principles of the Organisation for Economic Co-operation and Development. The best model split 2 possessed good fitting ability and internal as well as external predictive ability and it was used in explanation of activity trends of different classes of designer drugs.

Antiadhesive natural products against uropathogenic E. coli: What can we learn from cranberry extract?

Ethnopharmacological relevanceExtracts from Cranberry fruits (Vaccinium macrocarpon) are traditionally used against urinary tract infections, mainly due to antiadhesive activity against uropathogenic E. coli (UPEC), but the exact mode of action and compounds, responsible for the activity, are unknown. Aim of the studyi. To investigate if cranberry extract acts only by a single component or must be assessed as a multi-active-compound preparation; ii to screen isolated cranberry-related natural products under in vitro conditions to pinpoint natural products with antiadhesive effects against UPEC, followed by in silico calculations (QSAR) to predict potential antiadhesive compounds; iii. investigations by using urine samples from cranberry treated volunteers for evaluation on the bacterial transcriptome and the mannose-binding side of FimH, iv. to investigate if besides Tamm Horsfall Protein induction in the kidney, the extract acts also directly against UPEC. Material and methodsAntiadhesive activity of 105 compounds was determined by flow cytometric adhesion assay (UPEC UTI89 on T24 bladder cells). Urine samples from 16 volunteers treated with cranberry extract (p.o., 7 days, 900 mg/day) were used for ex vivo testing concerning influence on the bacterial transcriptome (Illumina RNA-seq) and interaction with the mannose binding domain of type-1 fimbriae. Resultsi. The antiadhesive effect of cranberry extract cannot be attributed to a single compound or to a single fraction. ii. Unglycosylated flavones and flavonols with bulky substitution of the B ring contribute to the antiadhesive activity. 3′-8″-biflavones and flavolignans (not related to cranberry fruits) were identified as potent antiadhesive compounds against UPEC. iii. QSAR yielded a model with good statistical performance and sufficient internal and external predictive ability. iv. Urine samples from male cranberry-treated volunteers indicated significant interaction with the mannose binding domain of type-1 fimbriae, which correlated with the amount of Tamm-Horsfall Protein in the test samples. v Cranberry extract did not influence the UPEC transcriptome; gene expression of bacterial adhesins (P-, S-fimbrae, curli) was not influenced by the urine samples, while a slight, but non-significant upregulation of type 1 fimbriae was observed. ConclusionsB-ring substituted flavones and flavonols from cranberry contribute to the antiadhesive activity against UPEC by inhibition of the FimH-mediated interaction with the host cell bladder epithelium.

Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms

Biodegradation is a significant process for removing organic chemicals from water, soil and sediment environments, and therefore biodegradability is critical to evaluate the environmental persistence of organic chemicals. In this study, based on a dataset with 171 compounds, four quantitative structure-activity relationship (QSAR) models were developed for predicting primary and ultimate biodegradation rate rating with multiple linear regression (MLR) and support vector machine (SVM) algorithms. Two MLR models were built with a dataset with carbon atom number ≤9, and two SVM models were built with a dataset with carbon atom number >9. In the MLR models, nArX (number of X on aromatic ring) is the most important descriptor governing primary and ultimate biodegradation of organic chemicals. For the SVM models, determination coefficient (R2) values, cross-validated coefficients (Q2LOO) and external validation coefficient (Q2ext) values are over 0.9, indicating the SVM models have satisfactory goodness-of-fit, robustness and external predictive abilities. The applicability domains of these models were visualized by the Williams plot. The developed models can be used as effective tools to predict biodegradability of organic chemicals.

Computational strategies towards developing novel antimelanogenic agents

AimsExtrinsic ageing or photoageing relates to the onset of age-linked phenotypes such as skin hyperpigmentation due to UV exposure. UV induced upregulated production of tyrosinase enzyme, which catalyses the vital biochemical reactions of melanin synthesis is responsible for the inception of skin hyperpigmentation. We aimed to generate a validated QSAR model with a dataset consisting of 69 thio-semicarbazone derivatives to elucidate the physicochemical properties of compounds essential for tyrosinase inhibition and to identify novel lead molecules with enhanced tyrosinase inhibitory activity and bioavailability. Main methodsLead optimization and insilico approaches were employed in this research work. QSAR model was generated and validated by exploiting Multiple Linear Regression method. Prioritization of lead-like compounds was accomplished by performing multi parameter optimization depleting molecular docking, bioavailability assessments and toxicity prediction for 69 compounds Derivatives of best lead compound were retrieved from chemical spaces. Key findingsMolecular descriptors explicated the significance of chemical properties essential for chelation of copper ions present in the active site of tyrosinase protein target. Further, derivatives which comprise of electron donating groups in their chemical structure were predicted and analysed for tyrosinase inhibitory activity by employing insilico methodologies including chemical space exploration. SignificanceOur research work resulted in the generation of a validated QSAR model with higher degree of external predictive ability and significance to tyrosinase inhibitory activity. We propose 11 novel derivative compounds with enhanced tyrosinase inhibitory activity and bioavailability.

A Double-Activity (Green Algae Toxicity and Bacterial Genotoxicity) 3D-QSAR Model Based on the Comprehensive Index Method and Its Application in Fluoroquinolones' Modification.

The comparative molecular similarity index analysis (CoMSIA) model of double-activity quinolones targeting green algae toxicity and bacterial genotoxicity (8:2) was constructed in this paper on the basis of the comprehensive index method. The contour maps of the model were analyzed for molecular modifications with high toxicities. In the CoMSIA model, the optimum number of components n was 7, the cross-validated q2 value was 0.58 (>0.5), the standard deviation standard error of estimate (SEE) was 0.02 (<0.95), F was 1265.33, and the non-cross-validated R2 value was 1 (>0.9), indicating that the model had a good fit and predicting ability. The scrambling stability test parameters Q2, cross-validated standard error of prediction (cSDEP), and dq2/dr2yy were 0.54, 0.25, and 0.8 (<1.2), respectively, indicating that the model had good stability. The external verification coefficient r2pred was 0.73 (>0.6), and standard error of prediction (SEP) was 0.17, indicating that the model had a good external prediction ability. The contribution rates of the steric fields, electrostatic fields, hydrophobic fields, hydrogen bond donor, and acceptor fields were 10.9%, 19.8%, 32.7%, 13.8%, and 22.8%, respectively. Large volume groups were selected for modification of ciprofloxacin (CIP), and the derivatives with increased double-activity characterization values were screened; the increase ratio ranged from 12.31–19.09%. The frequency of derivatives were positive and total energy, bioaccumulation, and environmental persistence was reduced, indicating that the CIP derivatives had good environmental stability and friendliness. Predicted values and CoMSIA model constructed of single activities showed that the CoMSIA model of double activities had accuracy and reliability. In addition, the total scores of the derivatives docking with the D1 protein, ferredoxin-NADP (H) reductases (FNRs), and DNA gyrase increased, indicating that derivatives can be toxic to green algae by affecting the photosynthesis of green algae. The mechanism behind the bactericidal effect was also explained from a molecular perspective.

Modeling and Prediction of Gas Chromatography Relative Retention Times of Volatile Organic Compounds

The theme of this paper is to foresee relative retention time of 122 volatile organic compounds. QSRR analysis was accomplished on a serial of 122 VOCs. Multiple Linear Regression (MLR) and support vector machine (SVM) methods were used to build linear and nonlinear (QSRR) models, respectively, which correlate the (RTT) values of these chemical substance to their structural descriptors. At first, the data set was separated using Kennard and Stone algorithm into a training set (92 chemicals) and a test set (30 chemicals) for statistical external validation. The five-dimensional models were developed using as independent variables the theoretical descriptors derived from the DRAGON software during the application of the procedure GA (genetic algorithm) - VSS (Variable Subset Selection). The robustness and the predictive performance of the MLR model have been demonstrated by inside and outer statistical validation. Non-linear technique leads to the best QSRR model with good internal and external predictive abilities. It is based on support vector machines using the RBF function for the optimal parameters values. The values of and were 17, 0.2, 0.2, respectively.

Modeling and Prediction of Gas Chromatography Relative Retention Times of Volatile Organic Compounds

The theme of this paper is to foresee relative retention time of 122 volatile organic compounds. QSRR analysis was accomplished on a serial of 122 VOCs. Multiple Linear Regression (MLR) and support vector machine (SVM) methods were used to build linear and nonlinear (QSRR) models, respectively, which correlate the (RTT) values of these chemical substance to their structural descriptors. At first, the data set was separated using Kennard and Stone algorithm into a training set (92 chemicals) and a test set (30 chemicals) for statistical external validation. The five-dimensional models were developed using as independent variables the theoretical descriptors derived from the DRAGON software during the application of the procedure GA (genetic algorithm) - VSS (Variable Subset Selection). The robustness and the predictive performance of the MLR model have been demonstrated by inside and outer statistical validation. Non-linear technique leads to the best QSRR model with good internal and external predictive abilities. It is based on support vector machines using the RBF function for the optimal parameters values. The values of and were 17, 0.2, 0.2, respectively.

Identification of prodigious and under-privileged structural features for RG7834 analogs as Hepatitis B virus expression inhibitor

In the present work, QSAR (quantitative structure−activity relationship) analysis has been executed for RG7834 analogs. RG7834 is a first-in-class selective and orally available dihydroquinolizinone (DHQ)-based small molecule Hepatitis B virus expression inhibitor. OECD’s guidelines have been followed for developing multiple QSAR models for Hepatitis B virus expression inhibitory activity of 73 RG7834 analogs. The present multiple QSAR models are not only easily interpretable but possess high external predictive ability, as well. These are effective in the recognition of many privileged and underprivileged molecular descriptors, which could be very valuable for the use of these models by the experts and nonexperts of QSAR in future optimizations. The models satisfy threshold values for many fitting, internal and external validation parameters, such as R2 = 0.83, Q2 = 0.80, CCCext = 0.88, etc., thereby demonstrating good external predictive ability of the models. The multiple QSAR and pharmacophoric models successfully identified a good number of important positively and negatively related structural features of RG7834 analogs that govern their Hepatitis B virus expression inhibitory activity. The results could be very beneficial to synthetic/medicinal chemists for future alterations of RG7834 analogs as better drug candidates.

In silico QSAR and molecular docking studies of sulfur containing shikonin oxime derivatives as anti-cancer agent for colon cancer

ObjectivesThis study is aimed at generating a statistically valid model using molecular descriptors and physicochemical parameters on sulfur-containing shikonin oxime derivatives and to have better understanding of binding affinity of these compounds as well as their various interactions with the target receptor so as to pave way for the designing of highly potent anti-colon cancer drug. Materials and methodsQuantitative structure–activity relationship and molecular docking studies were carried out on 40 dataset of sulfur containing shikonin oxime derivatives. Density functional theory were employed in optimizing the molecule with B3LYP/6-31G* as basis set, while descriptor selection and generation of multiple linear regression model that correlates the structural feature of the compounds to their experimental activities was done by employing Genetic Function Algorithm (GFA). The softwares that was used in this study include Chemdraw, Spartan software, Padel, Material studio, Pyrex, Vina wizard, Autoduck, as well as Discovery studio. ResultsAmong the models generated, the one with best statistical significance was selected and it has R2 value of 0.92, adjusted R2 value of 0.89, and leave one out (LOO) cross validation coefficient (Q2) value of 0.85. To confirm the predictive capacity of the selected model, external validation was carried out using the test set compounds and it has the R2ext value of 0.88. Molecular docking study revealed that all the compounds have favorable binding affinity to the target receptor, compound number 23 has the highest binding affinity of −8.3 kcal/mol and favorable number of various interactions. ConclusionBased on the parameters of internal and external validation, the robustness, stability and predictive ability of the selected model was verified, compounds number 23 with highest binding affinity and favorable number of various interactions can be used as lead compound in the feature design of highly potent anti-colon cancer drug.

A Quick and Efficient Non-Targeted Screening Test for Saffron Authentication: Application of Chemometrics to Gas-Chromatographic Data.

Saffron is one of the most adulterated food products all over the world because of its high market prize. Therefore, a non-targeted approach based on the combination of headspace flash gas-chromatography with flame ionization detection (HS-GC-FID) and chemometrics was tested and evaluated to check adulteration of this spice with two of the principal plant-derived adulterants: turmeric (Curcuma longa L.) and marigold (Calendula officinalis L.). Chemometric models were carried out through both linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) from the gas-chromatographic data. These models were also validated by cross validation (CV) and external validation, which were performed by testing both models on pure spices and artificial mixtures capable of simulating adulterations of saffron with the two adulterants examined. These models gave back satisfactory results. Indeed, both models showed functional internal and external prediction ability. The achieved results point out that the method based on a combination of chemometrics with gas-chromatography may provide a rapid and low-cost screening method for the authentication of saffron.

2D QSAR Analysis of Substituted Quinoxalines for their Antitubercular and Antileptospiral Activities.

The Quantitative structure activity relationship for thirty two novel substituted quinoxalines was performed for their antitubercular (Mycobacterium tuberculosis H37Rv) and antileptospiral (Leptospirainterrogans) activities. The quinoxalines were substituted with azetidinones, thiazolidinones and fluoroquinolones. Several compounds exhibited good activity against both the infections and they all possess fluoroquinolone moiety with the quinoxaline. The models developed showed good linear relationship (r2 = 0.71-0.88), with an internal predictive ability (q2> 0.61) and good external predictive ability (pred_r2>0.71). The compounds were separated into a training set on which regression was performed and a test set on which the predictive ability of the model was tested. Other statistical parameters including Ro2, Ro'2, k, k' and Z- score were in the acceptable range. The descriptors obtained explained the necessity of spatial orientation of atoms including branching and adjacency, presence of electronegative groups, balance between lipophilic elements and their binding strengths.

Molecular Virtual Screening Studies of Herbicidal Sulfonylurea Analogues Using Molecular Docking and Topomer CoMFA Research

A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of 45 sulfonylurea herbicidals is established using the Topomer comparative molecular field analysis (Topomer CoMFA). The results show that correlation coefficient (q2), non-cross-validation correlation coefficient (r2), and external validation (Q ext 2 ) are 0.884, 0.908, and 0.857 respectively. The biological evaluation demonstrates that the obtained model has the excellent external predictive ability and good estimation stability. The methodology of the fragment-based drug design is also applied to design new sulfonylurea herbicides using the Topomer Search technology. Compound 11 with the highest activity is chosen as the template molecule. By adding three substitutional groups selected from the ZINC database to the template molecule, 36 new compounds with an activity more than that of the template molecule are obtained. The template molecular and designed compounds are used to explore the binding relationship of the ligands with the receptor protein in molecular docking. The result shows that hydrogen bonding interactions form between the ligands and amino acid residues.

Molecular virtual screening studies of herbicidal sulfonylurea analogues using molecular docking and topomer CoMFA research

A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of 45 sulfonylurea herbicidals is established using the Topomer comparative molecular field analysis (Topomer CoMFA). The results show that correlation coefficient (q2), non-cross-validation correlation coefficient (r2), and external validation (Qext2) are 0.884, 0.908, and 0.857 respectively. The biological evaluation demonstrates that the obtained model has the excellent external predictive ability and good estimation stability. The methodology of the fragment-based drug design is also applied to design new sulfonylurea herbicides using the Topomer Search technology. Compound 11 with the highest activity is chosen as the template molecule. By adding three substitutional groups selected from the ZINC database to the template molecule, 36 new compounds with an activity more than that of the template molecule are obtained. The template molecular and designed compounds are used to explore the binding relationship of the ligands with the receptor protein in molecular docking. The result shows that hydrogen bonding interactions form between the ligands and amino acid residues.

QSAR Studies of amino-pyrimidine derivatives as Mycobacterium tuberculosis Protein Kinase B inhibitors

Quantitative structure activity relationship (QSAR) analysis was applied to a series of amino-pyrimidine derivatives as PknB inhibitors using a combination of various physicochemical and quantum descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the chemotherapeutic activity of the amino-pyrimidine derivatives. Good agreement between experimental and predicted activity values, obtained in the validation procedure, indicated the good quality of the derived QSAR model. The statistically significant best QSAR model has a cross validated correlation coefficient R2CV= 0.973 and external predictive ability of prediction R2 = 0.778 was developed by MLR. The proposed model has good robustness and predictability when verified by internal and external validation.

Complementary Quantitative Structure⁻Activity Relationship Models for the Antitrypanosomal Activity of Sesquiterpene Lactones.

Three complementary quantitative structure–activity relationship (QSAR) methodologies, namely, regression modeling based on (i) “classical” molecular descriptors, (ii) 3D pharmacophore features, and (iii) 2D molecular holograms (HQSAR) were employed on the antitrypanosomal activity of sesquiterpene lactones (STLs) toward Trypanosoma brucei rhodesiense (Tbr), the causative agent of the East African form of human African trypanosomiasis. In this study, an extension of a previous QSAR study on 69 STLs, models for a much larger and more diverse set of such natural products, now comprising 130 STLs of various structural subclasses, were established. The extended data set comprises a variety of STLs isolated and tested for antitrypanosomal activity within our group and is furthermore enhanced by 12 compounds obtained from literature, which have been tested in the same laboratory under identical conditions. Detailed QSAR analyses yielded models with comparable and good internal and external predictive ability. For a set of compounds as chemically diverse as the one under study, the models exhibited good coefficients of determination (R2) ranging from 0.71 to 0.85, as well as internal (leave-one-out Q2 values ranging from 0.62 to 0.72) and external validation coefficients (P2 values ranging from 0.54 to 0.73). The contributions of the various tested descriptors to the generated models are in good agreement with the results of previous QSAR studies and corroborate the fact that the antitrypanosomal activity of STLs is very much dependent on the presence and relative position of reactive enone groups within the molecular structure but is influenced by their hydrophilic/hydrophobic properties and molecular shape.

Computational studies on N-phenyl pyrrole derivatives as MmpL3 inhibitors in Mycobacterium tuberculosis

The fight against tuberculosis (TB) is a time immemorial one and the emergence of new drug resistant strains of Mycobacterium tuberculosis keeps throwing new challenges to the scientific community immersed in finding mechanisms to control this dreaded disease. Computer aided drug designing (CADD) is one of the several approaches that can assist in identifying the potent actives against Mycobacterium. In this work, a series of 109 known Mycobacterial membrane proteins large 3 (MmpL3) inhibitors were pooled and atom based 3D QSAR analysis was performed to understand the structural features essential for inhibitory activity against the MmpL3, known to be a key player in transporting substances critical for cell wall integrity of Mycobacterium. The data set employed was randomly split into training set and test set molecules. The training set of 74 molecules was used to derive CoMFA and CoMSIA models that were statistically reliable (CoMFA: q2loo = 0.53; r2ncv = 0.93 and CoMSIA: q2loo = 0.60; r2ncv = 0.93). The derived models also exhibited good external predictive ability (CoMFA: r2pred = 0.78 and CoMSIA: r2pred = 0.79). The results are quite encouraging and information derived from these analyses was applied to design new molecules. The designed molecule showed appreciable predicted activity values and reasonably good ADMET profile. The strategy used in designing new molecules can be pursued in the hunt for new chemical entities targeting MmpL3, expanding the existing arsenal against TB.

Docking and 3D QSAR Studies on Substituted Cyclobutylphenyl Quinoline Derivatives as Inhibitors of Bacterial DNA Gyrase.

Docking and 3D QSAR studies were performed on Inhibitor of Bacterial DNA gyrase to develop a predictive Common Pharmacophore Hypothesis (CPH). Molecular interaction and binding affinities of these analogues, differing with nucleotides and amino acids are studied are performed. Biological activity is predicted by QSAR approach by relating the set of compounds. QSAR study was applied using PHASE, the docking studies were performed using Glide module and Prime/MM-GB/SA is used for free energy ligand binding calculations. We obtained high pred_r2 value (pred_r2= 0. 9239), suggesting a significant external predictive ability of the QSAR model. Mutant docking score is -9. 023. In Wild type docking it shows lower binding energy and binding affinity is a decrease so, docking score of wild most dock 26 molecule is - 6.093. Protein shows various sites for molecular interaction, so from MM-GBSA. In the present study, development of predictive hypothesis (CPH) for 4-substituted cyclobutylphenyl quinoline derivatives and CPH were generated and results were interpreted on a developed model containing five features gives good and predictive statistical significance. Docking studies explained hydrogen bonding, pi-pi stacking with deoxy-ribonucleotide and with Ala 1120 and positive charge with deoxyribonucleotide. MM-GBSA explains the binding affinity of most docked ligand with protein.

Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors

In the present work, OECD guidelines have been followed for developing QSAR (Quantitative Structure-Activity Relationship) models for anti-HAT (Human African trypanosomiasis) activity of two hundred and seventy pyrazole sulphonamides. The newly developed easily interpretable multiple QSAR models have been successful in identification of many privileged as well as under-privileged molecular descriptors, which could be highly useful for future use of these models by expert and non-experts of QSAR. The multiple QSAR models satisfy threshold values for many statistical parameters such as R2 = 0.80–0.83, Q2 = 0.79–0.81, CCCext = 0.82–0.84, etc. thereby assuring good external predictive ability of the models. The multiple QSAR and pharmacophoric models successfully identified that N,4-disubstituted-benzenesulfonamide moiety, frequency of occurrence of H-bond acceptor from Oxygen atom within nine bonds and some other pharmacophoric features that govern the anti-HAT activity of pyrazole sulphonamides. The results could be very useful to synthetic/medicinal chemists for future modifications of pyrazole sulphonamides as drug candidates.