Quantitative Structure-activity Relationship Techniques Research Articles

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.

PARP1 is one of six enzymes required for the highly error-prone DNA repair pathway microhomology-mediated end joining (MMEJ) and needs to be inhibited when over-expressed. In order to study the PARP1 inhibitory effect of fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives (FTPDDs) by quantitative structure-activity relationship technique, six models were established by four kinds of methods, heuristic method, gene expression programming, random forester, and support vector regression with single, double, and triple kernel function respectively. The single, double, and triple kernel functions were RBF kernel function, the integration of RBF and polynomial kernel functions, and the integration of RBF, polynomial, and linear kernel functions respectively. The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. Among the models, the model established by support vector regression with triple kernel function, in which the optimal R 2 and RMSE of training set and test set were 0.9353, 0.9348 and 0.0157, 0.0288, and R2 cv of training set and test set were 0.9090 and 0.8971, shows the strongest prediction ability and robustness. The method of support vector regression with triple kernel function is a great promotion in the field of quantitative structure-activity relationship, which will contribute a lot to designing and screening new drug molecules. The information contained in the model can provide important factors that guide drug design. Based on these factors, six new FTPDDs have been designed. Using molecular docking experiments to determine the properties of new derivatives, the new drug was ultimately successfully designed.

Anticancer Drug Design: Development of Cyclin-Dependent Kinase Inhibitors Using In Silico Techniques

In mammalian cells, proliferation is controlled by the cell cycle, where cyclin-dependent kinases regulate critical checkpoints. CDK4 is considered a highly validated anticancer drug target due to its essential role in regulating cell cycle progression at the G1 restriction point. Our objective is to design novel CDK4 inhibitors using Structure-Based Drug Design and Quantitative Structure-Activity Relationship techniques. We used bioinformatics tools and biological databases. QSAR study of CDK4 inhibitors has given us an idea of the physicochemical features of studied compounds and their correlation with the IC50 activity. The docking study has helped to highlight the molecule key elements to refine in order to get a more potent compound of CDK4.The Molecule under the code 21366124 which has the low IC50= 3 nmole shows the most binding affinity with a score value of ΔG=-9,8 kcal/mol. As prospects, it would be very interesting to synthesize this drug candidate and to test its inhibitory activity on cell culture of breast cancer.

Toxicity Detection in Drug Candidates using Simplified Molecular-Input Line-Entry System

The need for analysis of toxicity in new drug candidates and the requirement of doing it fast have asked the consideration of scientists towards the use of artificial intelligence tools to examine toxicity levels and to develop models to a degree where they can be used commercially to measure toxicity levels efficiently in upcoming drugs. Artificial Intelligence based models can be used to predict the toxic nature of a chemical using Quantitative Structure Activity Relationship techniques. Convolutional Neural Network models have demonstrated great outcomes in predicting the qualitative analysis of chemicals in order to determine the toxicity. This paper goes for the study of Simplified Molecular Input Line-Entry System (SMILES) as a parameter to develop Long short term memory (LSTM) based models in order to examine the toxicity of a molecule and the degree to which the need can be fulfilled for practical use alongside its future outlooks for the purpose of real world applications.

Anticancer Drug Design: Development of Cyclin-Dependent Kinase Inhibitors Using In Silico Techniques

In mammalian cells, proliferation is controlled by the cell cycle, where cyclin-dependent kinases regulate critical checkpoints. CDK4 is considered highly validated anticancer drug target due to its essential role regulating cell cycle progression at the G1 restriction point. Our objective is designing novel CDK4 inhibitors using Structure-Based Drug Design and Quantitative Structure-Activity Relationship techniques. We used bioinformatics tools and biological databases. QSAR study of CDK4 inhibitors has given us an idea on the physicochemical features of studied compounds and their correlation with the IC50 activity. The docking study has helped to highlight the molecule key elements to refine in order to get a more potent compound of CDK4.The Molecule under the code 21366124 which has the low IC50= 3 nmole shows the most binding affinity with score value of ΔG=-9,8 kcal/mol. As prospects, it would be very interesting to synthesize this drug candidate and to test its inhibitory activity on cell culture of breast cancer

Investigation of MI-2 analogues as MALT1 inhibitors to treat of diffuse large B-Cell lymphoma through combined molecular dynamics simulation, molecular docking and QSAR techniques and design of new inhibitors

Mucosa-associated lymphoid tissue lymphoma translocation protein-1 (MALT1) has emerged as a promising therapeutic target for the treatment of activated B cell–like diffuse large B-cell lymphoma (ABC-DLBCL). To develop more potent MALT1 inhibitors, a series of MI-2 analogues were investigated using combined molecular modeling techniques such as molecular dynamics (MD) simulation, molecular docking and quantitative structure-activity relationship (QSAR). 40 ns MD simulations were successfully performed on the docked complexes of reference compound (MI-2) and compound 33 (one of highly active compounds of the data set) to investigate the binding stability and specifying the active site of the MALT1 enzyme in interaction with inhibitors. A few key residues (GLY416 and GLU500) at the binding site of MALT1 enzyme were identified. Using molecular docking was understood that hydroxyl groups along with groups of dichlorophenyl, chlorobenzyl and fluorobenzyl help to create interactions between the MI-2 analogues and enzyme residues to stabilize the conformation of compounds at the binding pocket. A robust and reliable QSAR model, as evidenced by the high R2, Q2LOO and R2ext values (0.933, 0.891 and 0.970, respectively), was gained based on the MI-2 analogues to predict the quantitative chemical-biological relationship and predict the inhibitory activity of new drug compounds. Then ten new MI-2 analogues have been designed that provide better inhibitory activity with the MALT1 enzyme than the reference compounds.

Study of the antimalarial activity of 4-aminoquinoline compounds against chloroquine-sensitive and chloroquine-resistant parasite strains

This study is concerned with identifying features of 4-aminoquinoline scaffolds that can help pinpoint characteristics that enhance activity against chloroquine-resistant parasites. Statistically valid predictive models are reported for a series of 4-aminoquinoline analogues that are active against chloroquine-sensitive (NF54) and chloroquine-resistant (K1) strains of Plasmodium falciparum. Quantitative structure activity relationship techniques, based on statistical and machine learning methods such as multiple linear regression and partial least squares, were used with a novel pruning method for the selection of descriptors to develop robust models for both strains. Inspection of the dominant descriptors supports the hypothesis that chemical features that enable accumulation in the food vacuole of the parasite are key determinants of activity against both strains. The hydrophilic properties of the compounds were found to be crucial in predicting activity against the chloroquine-sensitive NF54 parasite strain, but not in the case of the chloroquine-resistant K1 strain, in line with previous studies. Additionally, the models suggest that ‘softer’ compounds tend to have improved activity for both strains than do ‘harder’ ones. The internally and externally validated models reported here should also prove useful in the future screening of potential antimalarial compounds for targeting chloroquine-resistant strains.Graphical Predictive models reveal linear relationships for activity of 4-aminoquinoline analogues active against chloroquine-sensitive strains of Plasmodium falciparum

Quantitative Structure-Activity Relationships of Aquatic Narcosis: A Review.

Prior estimation of toxicity of each and every, existing and yet to be synthesized chemicals is a must to elude their adverse effect on the environment. Experimental determination of such parameters is time consuming, cost effective and above all, it demands the sacrifice of many vertebrates. At this end, the REACH regulations advocate for the use of non-testing predictive methods such as read-across, weight-of-evidence and QSAR (quantitative structure-activity relationship) techniques. Among these methods, QSAR is found to be the best as it is based on molecular structure only. The descriptors used in deriving the model in QSAR vary according to the nature of the narcotics as well as the species used for. The success of a model in predicting the toxicity of a narcotic purely depends on the type of descriptors selected that explains the structural features closely related to the property under study. In this review, we have focused on the different types of descriptors and QSAR models used to explain the narcosis phenomenon. Literature was scanned for acute toxicity of chemicals on species like tadpoles, protozoa, planktonic crustaceans, and small fishes like million fish, rainbow fish etc. from different sources. The toxicity and toxicants were classified considering their polarity and specific interactions of the compounds. Due to complex nature of the substrate, the mechanism of action of toxicant is uncertain. However, the overall results obtained from the biological study have been subjected to QSAR studies to obtain various models, which can provide some ideas on the mode of toxicological action. Different types of molecular descriptors derived both experimentally and theoretically have been used in the QSAR studies. Mostly biochemicals have a specific signature on oil/water partition (Ko/w, P), which is the crux in biological activity. Accordingly, the toxicological activities have good correlations with log P. Addition of some more structural descriptors improves the structure-toxicity relationship. Among these, electronic descriptors like EHOMO, ELUMO and ΔE derived from molecular orbitals have been used in the QSAR. ELUMO describing the energy of excited species of the molecule is found to be the most suitable one. Other molecular descriptors used in the QSAR include constitutional, topological and Abraham's solute descriptors. The models derived from the QSAR studies were found to be highly significant to predict the toxicology as well as to throw light on the mechanism. The best descriptor for aquatic narcosis is the KO/W or P. Addition of an electronic parameter (ELUMO) improves the QSAR to some extent. However, substitution of ELUMO by other class of molecular descriptors has also some statistical significance. To have a global QSAR model, in addition to P, some more appropriate descriptors are to be derived either experimentally or theoretically, latter being the more cost effective and easy in derivation.

XMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles.

A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.

Performance of machine learning algorithms for qualitative and quantitative prediction drug blockade of hERG1 channel

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficits in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling techniques. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC50 values for large dataset of molecules and compared it to automated patch clamp systems for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R2 ∼0.8 for pIC50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in the cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS IN COMPUTER AIDED MOLECULAR DESIGN

The drug development process requires the complete evaluation and identification of the chosen substance as well as its properties. It involves extensive chemical examination to achieve the best therapeutic effects which demands huge expenditure both in terms of time and money. Computer aided molecular design (CAMD) allows the production of new substances with pre-decided properties. Additionally, in order to illustrate and determine the interrelationship between the chemical structure of a compound and its biological activity, Quantitative Structure Activity Relationship (QSAR) is applied by employing a mathematical model and arranging molecular descriptors. This paper presents review of CAMD and QSAR techniques. The most common chemometric techniques are also emphasized. CAMD and QSAR are considered to be extremely efficient instruments in molecular design and accelerate the initial steps of drug development process. Furthermore, they enhance the effectiveness and reduce the cost of newly developed drugs.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Recent Advances in Multi-Task QSAR Modeling for Drug Design

Computer-aided drug design may provide a massive scope to reduce the overall duration as well as the expenditure associated with drug discovery. Although the concept of Quantitative Structure Activity Relationship (QSAR) is almost fifty years old, it is still considered as a very useful method for finding novel therapeutic agents. In the last decade, the field of QSAR has witnessed great advances in terms of techniques, ideas and approaches. 3 Multi-task QSAR is one such approach that has a huge opportunity to contribute in the discovery of novel therapeutic agents. Classical QSAR techniques focus on the classical idea that one drug is aimed to act on one target. However, due to the highly complex and multi-factorial nature of some diseases like cancer, microbial infections, neurodegenerative and metabolic disorders, the classical idea of drug development frequently produces less satisfying effects. The effect of the inhibition of one specific target is compensated by different back-up or fail-safe mechanisms. Polypharmacology focuses on the concept of simultaneous inhibition of multiple targets by single therapeutic agents. Contrary to single target drugs that bind with a specific target with high affinity, multi-target agents are low affinity binders. Therefore, management of therapy is easier for the multi-target agents. Multi-task QSAR techniques are able to incorporate the ideas of polypharmacology. While conventional QSAR techniques are based on the datasets of compounds with a single target and same or similar assay conditions, multi-task QSAR can consider data on multiple targets and various assay conditions at a time. In addition, multi-task QSAR techniques are also useful to address the problems of fewer datapoints. QSAR is a statistical technique and a higher amount of data-points generates more reliable models. However, for several diseases the availability of a data is less and developed models on the available data may not be able to provide detailed insight into structural requirements of the molecules for higher potency. As multi-task QSAR models may be developed on the data collected from different assay conditions, more information may be retrieved for the explanation of structural patterns. Therefore, multi-task QSAR has emerged as one of the most essential tools in the last few years and various groups of researchers participated in the development of multi-task QSAR models. Though conventional 2D QSAR techniques have found several alternatives such as 3D QSAR, pharmacophore mapping as well as molecular docking, 2D-QSAR is unique in handling problems of multi-task modeling. Multi-task QSAR has been utilized for the developing models for anti-cancer, anti-bacterial 17 and neuroprotective agents. In addition, reports on multi-target modeling on kinase and ubiquitineproteosome pathways are also found. Furthermore, multi-task agents are also useful for understanding structural patterns required for toxicities of nanoparticles, environmental pollutants etc. Although development of multi-task QSAR model is quite similar to conventional modeling as far as descriptor calculation and model generation are concerned, the construction and management of a dataset is quite different. In most of the cases, classification models are generated with the data by suitable machine learning tools such as Linear Discriminant Analyses (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM) etc. Generally, a specific cut-off value for each system is fixed by the user to classify the data into ‘sensitive, and ‘non-sensitive’. Sometimes special statistical treatments, like averaging the descriptor values for specific system and calculation of moving average, are required for handling the complicated data. Some attempts were taken to develop regression based multi-target models. However, there still is a debate about the actual effectiveness of multi-target QSAR modeling as the size of a data set is sometimes so increased that the model may not be properly validated by conventional approaches. Moreover, there are concerns over applicability domain of the multitask models. Nevertheless, multi-task QSAR models may provide opportunity to widen the area of QSAR studies as well as to contribute to the investigation of different emerging areas such as nanotoxicity, polypharmacology, drug repositioning, environmental safety management etc.

State of the art in the application of QSAR techniques for predicting mixture toxicity in environmental risk assessment

The focus of regulatory chemical risk assessment has been mainly placed on single chemicals rather than mixtures. However, living organisms and the environment might be exposed to mixtures of chemicals. Many scientific studies have revealed that mixture toxicity can arise from the combined effects of components present at levels below their individual no-effect concentrations. Predictive approaches will be essential for estimating mixture toxicity, as the number of possible mixtures is extremely large. Although predictive models are virtually indispensable for estimating mixture toxicity for both scientific and regulatory purposes, risk assessors encounter substantial difficulties in using conventional models, mainly due to the lack of information on the modes of toxic action of the mixture constituents. Alternative models that use different information instead of the modes of action thus need to be developed. The objective of this study is to investigate the state of the art in predictive models based on quantitative structure–activity relationship techniques for estimating the toxicity of mixture components, and to identify future challenges hindering more reliable mixture risk assessment for environmental risk assessment. Alternative models need to be developed not only to overcome the limitations of conventional models, but also to improve their performance.

Editorial (Thematic Issue: Current Status and Perspective on Drug Targets in Tubercle Bacilli and Drug Design of Antituberculous Agents Based on Structure-Activity Relationship)

Editorial (Thematic Issue: Current Status and Perspective on Drug Targets in Tubercle Bacilli and Drug Design of Antituberculous Agents Based on Structure-Activity Relationship)

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.

Development of a chronic fish toxicity model for predicting sub-lethal NOEC values for non-polar narcotics

To comply with the REACH (Registration, Evaluation, Authorisation and restriction of Chemicals) regulations, the generation of chronic fish toxicity data is required for chemicals produced or imported within or into the EU in quantities greater than 100 tonnes per year. This comes at a great cost to industry and consumers alike and requires the sacrifice of many vertebrates. In acknowledgment of these issues the REACH regulations encourage the use of non-testing methods (NTM). These include read-across, weight-of-evidence and QSAR (quantitative structure–activity relationship) techniques. There are many QSAR tools available to generate predictive values for a number of physico-chemical properties, as well as human and environmental health end points; however, close analysis of the currently available chronic fish models identified room for improvement in both the selection of data used and in its application in model creation. In light of this a model was developed using only sub-lethal no-observed-effect concentration (NOEC) end-point data according to best practice QSAR development. Only the lowest value was taken for each compound, in line with the conservative approach taken by the European Chemicals Agency (ECHA). The model developed meets the Organisation for Economic Co-operation and Development (OECD) principles, has strong internal and external validation statistics, and can reliably predict sub-lethal chronic NOEC values for fish within its defined applicability domain.

Pharmacophore and QSAR modeling of some structurally diverse azaaurones derivatives as anti-malarial activity

Quantitative structure–activity relationship (QSAR) and Pharmacophore studies were performed on a series of 35 azaaurones derivatives to find out the structural requirements of their antimalarial activities. The compounds in the selected series were characterized by spatial, molecular and electrotopological descriptors using QSAR module of molecular design suite (V-Life MDSTM 3.5). Correlations between inhibitory activities and calculated predictor variables were established through partial least square regression method. The developed QSAR models were found to be statistically significant with respect to training (r 2 > 0.6), and cross-validation (q 2 > 0.6). Simulated annealing (SA) and stepwise (SW) regression are applied as variable selection methods for an effective comparison and model development. This study was performed with 35 compounds (data set) using the sphere exclusion (SE) algorithm method for the division of the data set into training and test sets. The statistically significant 2D-QSAR model having r 2 = 0.9061 and q 2 = 0.8150 with pred_r 2 = 0.8719 was developed by SW-PLS confirms a positive contribution of T_O_Cl_6, T_C_Cl_1, SsOHcount, T_C_F_2 and SsCH3E index to the anti-malarial activity and best Group-based QSAR (GQSAR) model having r 2 = 0.7624 and q 2 = 0.7341 with pred_r 2 = 0.7461 was developed by SA-PLS. The result two-dimensional QSAR and GQSAR clearly explained that substitution of topological indices, hydrophobic properties and auto-correlation descriptors of different atomic properties at R1 and R2 on aurone ring is essential for the activity. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by SA-partial least square method leading to anti-malarial activity prediction. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbor method, showing good correlative and predictive capabilities in terms of q 2 = 0.6906 and pred_r 2 = 0.7454. Additionally the pharmacophore model well corroborated with k-nearest neighbor studies as the contours of later were in good agreement with the 3D orientation of the pharmacophoric features. The distances between the pharmacophore sites were measured in order to confirm their significance to the activities. The results reveal that the acceptor, donor, and aromatic/hydrophobic pharmacophore properties are favorable contours sites for the activities. The results of 2D-QSAR, GQSAR and k-nearest neighbor-based 3D-QSAR studies give detailed structural insights as well as highlights important binding features of these substituted azaaurones derivatives as anti-malarial activity, which can provide guidance for the rational design of novel potent azaaurones derivatives.

QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes

A large series of pyrrolidine amides derivatives as DPP-IV inhibitors was subjected to quantitative structure–activity relationship (QSAR) analysis. These 248 geometrical structures were constructed and optimized at the HF/6-31G* level of theory by the Gaussian program. The 2D–QSAR model was developed from a training set consisting of 186 compounds by the minimum redundancy maximum relevance–sequential floating back–support vector regression method with a good determination coefficient: the squared correlation coefficient (Rtrain2 = 0.867) and the tenfold cross-validation squared correlation coefficient (qtrain-CV2 = 0.669). The QSAR model was then tested using an external test set consisting of 62 compounds and provided a satisfactory external predictive ability (Rtest2 = 0.666). 2D–QSAR model is robust and reliable when compared with 3D–QSAR techniques for the analogous compounds. According to the QSAR analysis, the electronic effect plays an important role for the substituents of the pyrrolidine and carbon rings. The study would serve as a guideline in designing more potent and selective drugs against type 2 diabetes.

Predictive Model Development for Adsorption of Aromatic Contaminants by Multi-Walled Carbon Nanotubes

In the present study, Quantitative Structure-Activity Relationship (QSAR) and Linear Solvation Energy Relationship (LSER) techniques were used to develop predictive models for adsorption of organic contaminants by multi-walled carbon nanotubes (MWCNTs). Adsorption data for 29 aromatic compounds from literature (i.e., the training data) including some of the experimental results obtained in our laboratory were used to develop predictive models with multiple linear regression analysis. The generated QSAR (r(2) = 0.88), and LSER (r(2) = 0.83) equations were validated externally using an independent validation data set of 30 aromatic compounds. External validation accuracies indicated the success of parameter selection, data fitting ability, and the prediction strength of the developed models. Finally, the combination of training and validation data were used to obtain a combined LSER equation (r(2) = 0.83) that would be used for predicting adsorption of a wide range of low molecular weight aromatics by MWCNTs. In addition, LSER models at different concentrations were generated, and LSER parameter coefficients were examined to gain insights to the predominant adsorption interactions of low molecular weight aromatics on MWCNTs. The molecular volume term (V) of the LSER model was the most influential descriptor controlling adsorption at all concentrations. At higher equilibrium concentrations, hydrogen bond donating (A) and hydrogen bond accepting (B) terms became significant in the models. The results demonstrate that successful predictive models can be developed for the adsorption of organic compounds by CNTs using QSAR and LSER techniques.

Structure-Based and Multiple Potential Three-Dimensional Quantitative Structure–Activity Relationship (SB-MP-3D-QSAR) for Inhibitor Design

The inhibitions of enzymes (proteins) are determined by the binding interactions between ligands and targeting proteins. However, traditional QSAR (quantitative structure-activity relationship) is a one-side technique, only considering the structures and physicochemical properties of inhibitors. In this study, the structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) is presented, in which the structural information of host protein is involved in the QSAR calculations. The SB-MP-3D-QSAR actually is a combinational method of docking approach and QSAR technique. Multiple docking calculations are performed first between the host protein and ligand molecules in a training set. In the targeting protein, the functional residues are selected, which make the major contribution to the binding free energy. The binding free energy between ligand and targeting protein is the summation of multiple potential energies, including van der Waals energy, electrostatic energy, hydrophobic energy, and hydrogen-bond energy, and may include nonthermodynamic factors. In the foundational QSAR equation, two sets of weighting coefficients {aj} and {bp} are assigned to the potential energy terms and to the functional residues, respectively. The two coefficient sets are solved by using iterative double least-squares (IDLS) technique in the training set. Then, the two sets of weighting coefficients are used to predict the bioactivities of inquired ligands. In an application example, the new developed method obtained much better results than that of docking calculations.