Quantitative Structure-activity Relationship Methodology Research Articles

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology.

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.

Reliable and accurate prediction of basic pK_a values in nitrogen compounds: the pK_a shift in supramolecular systems as a case study

This article presents a quantitative structure–activity relationship (QSAR) approach for predicting the acid dissociation constant (pK_a) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R^2) of 0.9905, low standard error (s) of 0.3066, and high Fisher statistic (F) of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK_a prediction, offering valuable insights for drug design and supramolecular system optimization.Graphical

Identification, Elucidation, and Toxicity Assessment of Nontarget Disinfection By-products from Fipronil Chlorination

In the present study, fipronil degradation was evaluated in typical chlorination conditions (fipronil: 20 μg L−1; free chlorine concentration: 2 mg L−1; pH 7.5; 25 ○C). Fipronil was quickly degraded by chlorination with half-time life of 2.08 min, achieving 95% of degradation in 15 min of reaction. For identification and elucidation of its disinfection by-products, fipronil solutions (2.0 mg L−1) were chlorinated at different contact times. Six by-products were identified and their structural formulas were elucidated by high-resolution mass spectrometry. Lastly, the toxicity of the unreacted and chlorinated solutions was evaluated using in vitro, in vivo, and in silico assays. Unreacted and chlorinated solutions were toxic to Artemia salina nauplii with an increase on toxic effects after 24 h of reaction time. No estrogenicity and cytotoxicity were observed for the tested solutions using re-engineered bioluminescent yeast estrogen screen and colorimetric MTT assays, respectively. According to quantitative structure-activity relationship methodology, chlorination may produce a more mutagenic compound than the precursor fipronil. The generated disinfection by-products also proved to be developmental toxicant, although it was not possible to measure their relative potency in relation to the parent compound.

Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications.

The increasing use of information technology in the discovery of new molecular entities encourages the use of modern molecular-modeling tools to help teach important concepts of drug design to chemistry and pharmacy undergraduate students. In particular, statistical models such as quantitative structure–activity relationships (QSAR)—often as its 3D QSAR variant—are commonly used in the development and optimization of a leading compound. We describe how these drug discovery methods can be taught and learned by means of free and open-source web applications, specifically the online platform www.3d-qsar.com. This new suite of web applications has been integrated into a drug design teaching course, one that provides both theoretical and practical perspectives. We include the teaching protocol by which pharmaceutical biotechnology master students at Pharmacy Faculty of Sapienza Rome University are introduced to drug design. Starting with a choice among recent articles describing the potencies of a series of molecules tested against a biological target, each student is expected to build a 3D QSAR ligand-based model from their chosen publication, proceeding as follows: creating the initial data set (Py-MolEdit); generating the global minimum conformations (Py-ConfSearch); proposing a promising mutual alignment (Py-Align); and finally, building, and optimizing a robust 3D QSAR models (Py-CoMFA). These student activities also help validate these new molecular modeling tools, especially for their usability by inexperienced hands. To more fully demonstrate the effectiveness of this protocol and its tools, we include the work performed by four of these students (four of the coauthors), detailing the satisfactory 3D QSAR models they obtained. Such scientifically complete experiences by undergraduates, made possible by the efficiency of the 3D QSAR methodology, provide exposure to computational tools in the same spirit as traditional laboratory exercises. With the obsolescence of the classic Comparative Molecular Field Analysis Sybyl host, the 3dqsar web portal offers one of the few available means of performing this well-established 3D QSAR method.

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?

Given a particular descriptor/method combination, some quantitative structure-activity relationship (QSAR) datasets are very predictive by random-split cross-validation while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here, we investigate, on in-house data, the relative usefulness of experimental error, distribution of the activities, and activity cliff metrics in determining how predictive a dataset is likely to be. We include unmodified in-house datasets, datasets that should be perfectly predictive based only on the chemical structure, datasets where the distribution of activities is manipulated, and datasets that include a known amount of added noise. We find that activity cliff metrics determine predictivity better than the other metrics we investigated, whatever the type of dataset, consistent with the modelability literature. However, such metrics cannot distinguish real activity cliffs due to large uncertainties in the activities. We also show that a number of modern QSAR methods, and some alternative descriptors, are equally bad at predicting the activities of compounds on activity cliffs, consistent with the assumptions behind "modelability." Finally, we relate time-split predictivity with random-split predictivity and show that different coverages of chemical space are at least as important as uncertainty in activity and/or activity cliffs in limiting predictivity.

Mechanistic Study of Anticancer Activity of Some Known Aminopyrimidoisoquinolinequinones via QSAR Classification Methodology

The impact of lipophilicity as represented by the logarithm of octanol/water partition coefficient (logP), the combined steric/polarizability effect as represented by molar refractivity (MR) and bulk as represented by molar volume (MV) on the biological activity of 29 known aminopyrimidoisoquinolinequinones APIQ were analyzed using quantitative structure activity relationships methodology (QSAR). The activity data chosen was the inhibitory concentration (IC50) against human gastric adenocarcinoma (AGS) cell line. On running regression analysis, the physicochemical parameters and IC50 show very weak correlations as evident by the low values of Pearson Correlation R2 (0.1 to 0.2). Since the individual compounds show appreciable activity (ranging from 20 to 0.5 μM), classification was resorted to in order to expose mechanistic nesting subgroups. This was done by clustering data points around various trend lines extracted from the scattered plot relating parameters to activity using R2 as an index. The correlation of IC50 versus MV was chosen a base of classification owing to higher statistical metrics it yield. This gave five regression lines, each of which is believed to represent a separate mechanistic profile. Additional descriptors were used to consolidate the clustering approach and to give depth to the assumed mechanistic profiles of each cluster.

QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies.

A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition; it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such research due to their high variability and the necessity of their monitoring to provide safety for agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of over 3000 descriptors were used in the quantitative structure-activity relationship (QSAR) analysis of binding activities (pIC50). A comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR CoMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q2 (‘leave-many-out’) is equal to 0.498, and for monoclonal antibodies, q2 is equal to 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space.

QSAR, Docking and Molecular Dynamics Studies on the Piperidone-grafted Mono- and Bis-spiro-oxindole-hexahydropyrrolizines as Potent Butyrylcholinesterase Inhibitors

ABSTRACT: Quantitative structure-activity relationship (QSAR) study on the piperidone-grafted mono- and bis-spirooxindole-hexahydropyrrolizines as potent butyrylcholinestrase (BuChE) inhibitors were carried out using statistical methods, molecular dynamics and molecular docking simulation. QSAR methodologies, including classification and regression tree (CART), multiple linear regression (MLR), principal component analysis (PCA) and principal component regression analysis (PCRA). Three descriptors in three classes: 3D-Morse, WHIM and GETAWAY descriptors were selected by SPSS software and then applied in the final tree structure to describe the inhibitory activities. Docking simulations were carried out using AutoDock Vina softwares for all inhibitors. Docking results showed that the studied BuChE inhibitors have two commons binding modes. Molecular dynamics results obtained by Gromacs showed that the more potent inhibitor has more interaction with the enzyme and higher effect on the enzyme structure.

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

QSAR: What Else?

QSAR (quantitative structure-activity relationship) is a method for predicting the physical and biological properties of small molecules; it is today in large use in companies and public services. However, as any scientific method, it is nowadays challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. Posing the question whether QSAR is a way not only to exploit available knowledge but also to build new knowledge, we shortly review QSAR history, thus searching for a QSAR epistemology. We consider the three pillars on which QSAR stands: biological data, chemical knowledge, and modeling algorithms. Most of the time we assume that biological data is a true picture of the world (as they result from good experimental practice), that chemical knowledge is scientifically true; so if a QSAR is not working, blame modeling. This opens the way to look at the role of modeling in developing scientific theories, and in producing knowledge. QSAR is a mature technology; however, debate is still active in many topics, in particular about the acceptability of the models and how they are explained. After an excursus in inductive reasoning, we relate the QSAR methodology to open debates in the philosophy of science.

Design (Docking and QSAR Studies) and synthesis of histone deacetylase 2 (HDAC2) inhibitors series

In recent years, it has been shown that histone deacetylase 2 (HDAC2) inhibitors increase histone acetylation and enhance memory processes, probably due to an increase in the gene transcription rate that emerges during memory formation. Histone acetylation generally favors long-term memory, whereas histone deacetylation impinges on it. However, until today there is no specific drug that can target the HDAC2 active site. In this work we applied the method of rational drug design, through enzyme-structural-chemical properties to generate new molecules as HDAC inhibitors. By the application of Quantitative Structure-Activity Relationship (QSAR) and molecular modeling methodologies our aim is to predict more potent HDAC inhibitors. 76 small molecules with potential activity were analyzed using QSAR methodology. The best model was constructed by merging the properties of electronegativity, atomic mass, polarizability, van der Waals forces and some conformational aspects, with the following statistical parameters: r 2 = 0.8935, q 2 LOO-CV = 0.8498, and q 2 LGO-CV = 0.7598. The molecular docking of the ligands on the template was performed by blind docking. The results showed intermolecular interactions between small molecules and some amino acids, such as His145, His146, Asp179, Asp186, and internal-H2O and Zn2+ of which IN01, IN04, and IN14 showed theoretically better biological activity compared with that of TSA and SAHA. Mainly, the IN14 synthesized molecule is a theoretical inhibitor of HDAC class I.

QSAR Modeling for Relative Toxicity Prediction of (3-(2-chloroquinolin-3-yl)oxiran-2-yl)(phenyl) methanone Derivatives

2-Chloroquinoline-3-carbaldehyde and its substituted products are extremely versatile intermediates for synthesizing a variety of compounds containing quinoline moiety, which find many pharmaceutical and other applications. Quantitative structure-activity relationship (QSAR) plays an important role in toxicity prediction. The present study deals with acute toxicity predictions LD50 (medianlethal dose) values of (3-(2-chloroquinolin-3-yl)oxiran-2-yl)(phenyl) methanone and its derivatives in rat by oral exposure through QSAR modelling software package T.E.S.T. In the present study the toxicity (LD50) is evaluated using a variety of QSAR methodologies, such as hierarchical clustering, the Food and Drug Administration (FDA) MDL, nearest neighbor and a consensus model. For compounds No. 1 to 4, 7, 10 and 11 hierarchical clustering method does not provide the LD50 values; however, other methods have successfully provided the toxicity estimation for the same. The said software helps to predict the exact LD50 values when compared to experimental data reported in the range (>2000 to >5000 mg/kg). This is a preliminary observation from screening of LD50 values using the said software package. Further study may be relevant using other software to compare the predicted data.

QSRR and QSAR Studies of Antitumor Drugs in View of their Biological Activity Prediction.

The QSRRs and QSARs are relatively new approaches to relate internal chemical structure and particular biological activity. This methodology is based on theory that mechanisms which took place into chromatography column are similar to those that occur in a living organism at the molecular level, for example when compounds penetrate into cells. In this paper, we aim to describe different cytostatic activities of selected anticancer drugs as QSRR and QSAR models, and prove usefulness of connected QSRR and QSAR methodology in different types of studies. Chromatographic experiments using gradient RP-HPLC method and different C18 stationary phases were performed. As a result we obtained retention parameter log kw. Moreover, to calculate descriptors, which characterize lipophilicity of analyzed antitumor drugs, DryLab program was utilized. Molecular modeling studies were performed by using HyperChem program. Dragon software was used to calculate structural descriptors, and then selected descriptors were used to build QSRR and QSAR models. Obtained data were analyzed by multiple regression analysis (MLR). Experimental log kw and predicted log kw from QSRR models developed, were further used in QSAR analysis. The goodness of fit was in the range of R2= 0.75 - 0.95, and the predictive performance of the models was Q2 = 0.6 - 0.81. Both QSRR and QSAR strategies presented in this paper, allowed predicting HPLC retention parameters and cytotoxic activities of anticancer medicines without the necessity to carry out time-consuming and expensive experimental tests.

Preliminary safety assessment of C-8 xylitol monoester and xylitol phosphate esters.

Most of the cosmetic compounds with preservative properties available in the market pose some risks concerning safety, such as the possibility of causing sensitization. Due to the fact that there are few options, the proper development of new molecules with this purpose is needed. Xylitol is a natural sugar, and the antimicrobial properties of xylitol-derived compounds have already been described in the literature. C-8 xylitol monoester and xylitol phosphate esters may be useful for the development of skincare products. As an initial screen for safety of chemicals, the combination of in silico methods and in vitro testing can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal and human testing. This study was designed to evaluate the safety of C-8 xylitol monoester and xylitol phosphate esters regarding carcinogenicity, mutagenicity, skin and eye irritation/corrosion and sensitization through alternative methods. For the initial safety assessment, quantitative structure-activity relationship methodology was used. The prediction of the parameters carcinogenicity/mutagenicity, skin and eye irritation/corrosion and sensitization was generated from the chemical structure. The analysis also comprised physical-chemical properties, Cramer rules, threshold of toxicological concern and Michael reaction. In silico results of candidate molecules were compared to 19 compounds with preservative properties that are available in the market. Additionally, in vitro tests (Ames test for mutagenicity, cytotoxicity and phototoxicity tests and hen's egg test--chorioallantoic membrane for irritation) were performed to complement the evaluation. In silico evaluation of both molecules presented no structural alerts related to eye and skin irritation, corrosion and sensitization, but some alerts for micronucleus and carcinogenicity were detected. However, by comparison, C-8 xylitol monoester, xylitol phosphate esters showed similar or better results than the compounds available in the market. Concerning experimental data, phototoxicity and mutagenicity results were negative. As expected for compounds with preservative activity, xylitol-derived substances presented positive result in cytotoxicity test. In hen's egg test, both molecules were irritants. Our results suggested that xylitol-derived compounds appear to be suitable candidates for preservative systems in cosmetics.

How good are ensembles in improving QSAR models? The case with eCoRIA

A conceptually new idea in quantitative structure–activity relationships (QSAR) which makes use of ensembles from molecular dynamics (MD) trajectories and information retrieved from enzyme–inhibitor binding thermodynamics is presented in this study. This new methodology, termed ensemble comparative residue interaction analysis (eCoRIA), attempts to overcome the current one chemical–one structure–one parameter value dogma in computational chemistry by modeling the biological activity as a function of molecular descriptors derived from an ensemble of conformers of enzyme–inhibitor complexes. The approach is distinctly different from the standard QSAR methodology which uses a single low-energy conformation or the properties averaged over a set of conformers to correlate with the activity. Each conformational ensemble derived from MD simulations is analyzed for the distribution of the non-bonded interaction energies (steric, electrostatic, and hydrophobic) along with solvation, strain, and entropic energy of the inhibitors with the individual amino acid residues in the receptor and these are correlated to the activity through a QSAR model. The scope of the new method is demonstrated with three diverse enzyme–inhibitor data-sets – glycogen phosphorylase b, human immunodeficiency virus-1 protease and cyclin-dependent kinase 2. The QSAR equations derived from the methodology have revealed all the structure activity relationships previously reported for these classes of molecules as well as uncovered some features that were hitherto unknown and may have a hidden role in driving the ligand–receptor-binding process. Impressive improvements in the predictions of affinity have been achieved compared to other QSAR formalisms namely CoMFA, CoMSIA (receptor-independent QSAR techniques), and CoRIA (a receptor-dependent QSAR technique). eCoRIA could provide an understanding of the thermodynamic properties influencing the ligand–receptor binding over a time scale as sampled by the MD simulation. The advantage of analyzing enzyme–inhibitor interaction energies in a statistical domain is that the noise due to inaccuracies in the potential energy functions can be reduced and mechanistically important interaction terms related to protein–ligand binding specificity can be identified which can assist the medicinal chemists in designing new molecules and biologists in studying the influence of position-specific mutations in the receptor on ligand binding.

Applying Quantitative Structure–Activity Relationship (QSAR) Methodology for Modeling Postmortem Redistribution of Benzodiazepines and Tricyclic Antidepressants

Postmortem redistribution (PMR) constitutes a multifaceted process, which complicates the interpretation of drug concentrations by forensic toxicologists. The present study aimed to apply quantitative structure-activity relationship (QSAR) analysis for modeling PMR data of structurally related drugs, 10 benzodiazepines and 10 tricyclic antidepressants. For benzodiazepines, an adequate QSAR model was obtained (R(2) = 0.98, Q(2) = 0.88, RMSEE = 0.12), in which energy, ionization and molecular size exerted significant impact. For tricyclic antidepressants, an adequate QSAR model with slightly inferior statistics (R(2) = 0.95, Q(2) = 0.87, RMSEE = 0.29) was established after exclusion of maprotiline, in which energy parameters, basicity character and lipophilicity exerted significant contribution. Thus, QSAR analysis could be used as a complementary tool to provide an informative illustration of the contributing molecular, physicochemical and structural properties in PMR process. However, the complexity, non-static and time-dependent nature of PMR endpoints raises serious concerns whether QSAR methodology could predict the degree of redistribution, highlighting the need for animal-derived PMR data.

Novel phenolic inhibitors of the sarco/endoplasmic reticulum calcium ATPase: identification and characterization by quantitative structure–activity relationship modeling and virtual screening

Inhibitors of the sarco/endoplasmic reticulum calcium ATPase (SERCA) are valuable research tools and hold promise as a new generation of anti-prostate cancer agents. Based on previously determined potencies of phenolic SERCA inhibitors, we created quantitative structure–activity relationship (QSAR) models using three independent development strategies. The obtained QSAR models facilitated virtual screens of several commercial compound collections for novel inhibitors. Sixteen compounds were subsequently evaluated in SERCA activity inhibition assays and 11 showed detectable potencies in the micro- to millimolar range. The experimental results were then incorporated into a comprehensive master QSAR model, whose physical interpretation by partial least squares analysis revealed that properly positioned substituents at the central phenyl ring capable of forming hydrogen bonds and of undergoing hydrophobic interactions were prerequisites for effective SERCA inhibition. The established SAR was in good agreement with findings from previous structural studies, even though it was obtained independently using standard QSAR methodologies.

Insight into molecular mechanism underlying the transesterification catalysed by penicillin G amidase (PGA) using a combination protocol of experimental assay and theoretical analysis

Penicillin G amidase (PGA) is one of the most recognised biocatalysts because of its critical application in the antibiotic industry. Herein, the additive effects involved in transesterification catalysed by PGA are explored in detail using a combination of experimental analysis and theoretical modelling. The transesterification ability of PGA is experimentally determined with 17 N-containing compounds as additives, and, on this basis, a series of quantitative structure–activity relationship (QSAR) models are developed from various physicochemical parameters characterising structural variation over the additives. The resulting models exhibit both good stability and predictive power, from which five most important properties that highlight structural basis and reaction mechanism underlying the transesterification are extracted, revealing that the topological property and electrostatic profile of additives exert a significant effect on reaction yield; the charge distribution around additive molecules is the most significant factor controlling reaction yield, and then the topological structure. Furthermore, it is inferred that the additive imidazole might constitute the catalytic triad of Ser, Glu or Asp involved in PGA active site, which appears similar to lipase, rendering PGA with the catalytic ability of transesterfication. The study highlights the potential application of QSAR methodology in the field of enzymatic regulator design.

Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions

The inclusion and accessibility of different methodologies to explore chemical data sets has been beneficial to the field of predictive modeling, specifically in the chemical sciences in the field of Quantitative Structure-Activity Relationship (QSAR) modeling. This study discusses using contemporary protocols and QSAR modeling methods to properly model two biomolecular systems that have historically not performed well using traditional and three-dimensional QSAR methodologies. Herein, we explore, analyze, and discuss the creation of a classification human Ether-a-go-go Related Gene (hERG) potassium channel model and a continuous Tetrahymena pyriformis (T. pyriformis) model using Support Vector Machine (SVM) and Support Vector Regression (SVR), respectively. The models are constructed with three types of molecular descriptors that capture the gross physicochemical features of the compounds: (i) 2D, 2 1/2D, and 3D physical features, (ii) VolSurf-like molecular interaction fields, and (iii) 4D-Fingerprints. The best hERG SVM model achieved 89% accuracy and the three-best SVM models were able to screen a Pubchem data set with an accuracy of 97%. The best T. pyriformis model had an R(2) value of 0.924 for the training set and was able to predict the continuous end points for two test sets with R(2) values of 0.832 and 0.620, respectively. The studies presented within demonstrate the predictive ability (classification and continuous end points) of QSAR models constructed from curated data sets, biologically relevant molecular descriptors, and Support Vector Machines and Support Vector Regression. The ability of these protocols and methodologies to accommodate large data sets (several thousands compounds) that are chemically diverse - and in the case of classification modeling unbalanced (one experimental outcome dominates the data set) - allows scientists to further explore a remarkable amount of biological and chemical information.

Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits.

The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified.