3D-QSAR illusions

Synthesis, antifungal evaluation, two-dimensional quantitative structure-activity relationship and molecular docking studies of isoxazole derivatives as potential fungicides

BACKGROUND

Sheath blight and bakanae disease, prominent among emerging rice ailments, exert a profound impact on rice productivity, causing severe impediments to crop yield. Excessive use of older fungicides may lead to the development of resistance in the pathogen. Indeed, a pressing and immediate need exists for novel, low-toxicity and highly selective fungicides that can effectively combat resistant fungal strains.

RESULTS

A series of 20 isoxazole derivatives were synthesized using alkoxy/halo acetophenones and N,N-dimethylformamidedimethylacetal. These compounds were characterized by various spectroscopic techniques, namely 1H nuclear magnetic resonance (NMR), 13C NMR and liquid chromatography-high-resolution mass spectrometry, and were evaluated for their fungicidal activity against Rhizoctonia solani and Fusarium fujikuroi. Compound 5n (5-(2-chlorophenyl) isoxazole) exhibited highest activity (effective dose for 50% inhibition [ED50] = 4.43 μg mL-1) against R. solani, while 5p (5-(2,4-dichloro-2-hydroxylphenyl) isoxazole) exhibited highest activity (ED50 = 6.7 μg mL-1) against F. fujikuroi. Two-dimensional quantitative structural-activity relationship (QSAR) analysis, particularly multiple linear regression (MLR) (Model 1), highlighted chi6chain and DistTopo as the key descriptors influencing fungicidal activity. Molecular docking studies revealed the potential of these isoxazole derivatives as novel fungicides targeting sterol 14α-demethylase enzyme, suggesting their importance as crucial intermediates for the development of novel and effective fungicides.

CONCLUSION

All test compounds were effective in inhibiting both fungi, according to the QSAR model, with various descriptors, such as structural, molecular shape analysis, electronic and thermodynamic, playing an important role. Molecular docking studies confirmed that these compounds can potentially replace commercially available fungicides and help control fungal pathogens in rice crops effectively. © 2024 Society of Chemical Industry.

Informed Use of 3D-QSAR for the Rational Design of Coumarin Derivatives as Potent and Selective MAO B Inhibitors

The quantitative structure-activity relationship method based on the three-dimensional structure of the target molecules (3D-QSAR) represents a valuable predictive tool for the design of new bioactive agents. Herewith, a detailed procedure is described which uses a pool comprising 67 derivatives substituted at position 4 and 7 of the common coumarin scaffold as a benchmark for deriving a predictive 3D-QSAR model employed for guiding the rational design of 10 new potent and selective MAO B inhibitors.

Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets

The screening of compounds for ADME-Tox targets plays an important role in drug design. QSPR models can increase the speed of these specific tasks, although the performance of the models highly depends on several factors, such as the applied molecular descriptors. In this study, a detailed comparison of the most popular descriptor groups has been carried out for six main ADME-Tox classification targets: Ames mutagenicity, P-glycoprotein inhibition, hERG inhibition, hepatotoxicity, blood–brain-barrier permeability, and cytochrome P450 2C9 inhibition. The literature-based, medium-sized binary classification datasets (all above 1,000 molecules) were used for the model building by two common algorithms, XGBoost and the RPropMLP neural network. Five molecular representation sets were compared along with their joint applications: Morgan, Atompairs, and MACCS fingerprints, and the traditional 1D and 2D molecular descriptors, as well as 3D molecular descriptors, separately. The statistical evaluation of the model performances was based on 18 different performance parameters. Although all the developed models were close to the usual performance of QSPR models for each specific ADME-Tox target, the results clearly showed the superiority of the traditional 1D, 2D, and 3D descriptors in the case of the XGBoost algorithm. It is worth trying the classical tools in single model building because the use of 2D descriptors can produce even better models for almost every dataset than the combination of all the examined descriptor sets.

Molecular Descriptors as a Facile Tool toward Designing Surface-Functionalized Nanoparticles for Drug Delivery

Modulating the surface chemistry of nanoparticles, often by grafting hydrophilic polymer brushes (e.g., polyethylene glycol) to prepare nanoformulations that can resist opsonization in a hematic environment and negotiate with the mucus barrier, is a popular strategy toward developing biocompatible and effective nano-drug delivery systems. However, there is a need for tools that can screen multiple surface ligands and cluster them based on both structural similarity and physicochemical attributes. Molecular descriptors offer numerical readouts based on molecular properties and provide a fertile ground for developing quick screening platforms. Thus, a study was conducted with 14 monomers/repeating blocks of polymeric chains, namely, oxazoline, acrylamide, vinylpyrrolidone, glycerol, acryloyl morpholine, dimethyl acrylamide, hydroxypropyl methacrylamide, hydroxyethyl methacrylamide, sialic acid, carboxybetaine acrylamide, carboxybetaine methacrylate, sulfobetaine methacrylate, methacryloyloxyethyl phosphorylcholine, and vinyl-pyridinio propanesulfonate, capable of imparting hydrophilicity to a surface when assembled as polymeric brushes. Employing free, Web-based, and user-friendly platforms, such as SwissADME and ChemMine tools, a series of molecular descriptors and Tanimoto coefficient of molecular pairs were determined, followed by hierarchical clustering analyses. Molecular pairs of oxazoline/dimethyl acrylamide, hydroxypropyl methacrylamide/hydroxyethyl methacrylamide, acrylamide/glycerol, carboxybetaine acrylamide/vinyl-pyridinio propanesulfonate, and sulfobetaine methacrylate/methacryloyloxyethyl phosphorylcholine were clustered together. Similarly, the molecular pair of hydroxypropyl methacrylamide/hydroxyethyl methacrylamide demonstrated a high Tanimoto coefficient of >0.9, whereas the pairs oxazoline/vinylpyrrolidone, acrylamide/dimethyl acrylamide, acryloyl morpholine/dimethyl acrylamide, acryloyl morpholine/hydroxypropyl methacrylamide, acryloyl morpholine/hydroxyethyl methacrylamide, carboxybetaine methacrylate/sulfobetaine methacrylate, and glycerol/hydroxypropyl methacrylamide had a Tanimoto coefficient of >0.8. The analyzed data not only demonstrated the ability of such in silico tools as a facile technique in clustering molecules of interest based on their structure and physicochemical characteristics but also provided vital information on their behavior within biological systems, including the ability to engage an array of possible molecular targets when the monomers are self-assembled on nanoparticulate surfaces.

Topomer-CoMFA proposed as a tool to construct dual EGFR/HER-2 models

Protein kinases (in this case, HER-2 and EGFR) are involved in cancer-related diseases. Some reports have shown unique CoMFA models using the sum of activities expressed as pIC₅₀ (-log IC₅₀), as the classical CoMFA technique would not be the best strategy to construct models for multitarget therapy considering that the molecular alignment will not be the same for different targets. An alternative for this problem is the use of Topomer-CoMFA, a variation of CoMFA, which does not require the alignment step in the generation of 3D models. In this study, we propose the combined use of the sum of activities and Topomer-CoMFA for the construction of a unique dual 3D model considering the inhibitory activities against EGFR and HER-2. For this, 88 compounds from the literature were divided into two groups: training (71) and test (17) sets. The biological activity of each compound, expressed as IC₅₀ for EGFR and HER-2, was transformed into pIC₅₀, summed, and used as the dependent variable in the Topomer-CoMFA analyses. The obtained model was considered statistically robust in the prediction of the dual activity of new compounds. Finally, based on the obtained model, we proposed structural modifications to some of the compounds used to improve the biological data. From the 3D model, we suggested new derivative compounds with improved biological activity for both targets. Therefore, the combination of the techniques proposed in this study proves to be a good strategy to construct better statistical models that can predict biological activities in multitarget systems.

Developing a variation of 3D-QSAR/MD method in drug design

In continuation of the previous reports on a combination of 3D-quantitative structure-activity relationships (QSAR) with computational molecular dynamics (MD) studies, a new variation of 3D-QSAR/MD method has been employed for drug-design as an alternative or supplementary for the typical experimental methods. The presented method is more cost-effective and less time-consuming than the previous methods and avoids several restrictions of experimental methods, such as validity estimation, and predictability. For this purpose, seven inhibitors for bromodomain (BRD)-containing protein, as an important protein in the development of different types of cancer and responsible for oncogenic rearrangements, have been selected to study of their interactions by docking and MD simulations using molecular mechanics/generalized born surface area (MM/GBSA) method. To build the proposed model, a common variant of 3D-QSAR methods, comparative molecular field analysis has been employed using a dataset of 100 MD-extracted ligand conformations and their corresponding MM/GBSA BRD4-binding energies. The results showed excellent predictability of the generated model for both the training set and test groups. Finally, two new inhibitors were selected among total 4000 designed derivatives (generated through evolutionary techniques) using the proposed 3D-QSAR-MD model. The potentials of these inhibitors were assessed by MD simulations, which showed the higher inhibitory of these compounds than the previous inhibitors. Therefore, this method showed high potentials for acceleration of the procedure of drug design and a basis for joining researchers in computational biology and pharmaceutical sciences.

Synthesis, Biological Evaluation, and QSAR Studies of 3-Iodochromone Derivatives as Potential Fungicides

Despite the emergence of novel biotechnological and biological solutions, agrochemicals continue to play an important role in crop protection. Fungicide resistance is becoming a major problem; numerous cases of fungicide resistance have occurred worldwide in the last decade, resulting in the loss of several fungicides. The discovery of new molecules has therefore assumed critical importance in crop protection. In our quest for biologically active molecules, we herein report the synthesis of a series of twenty-one 3-Iodochromone derivatives (4a-4u), in a two-step process by condensation of 2-hydroxyacetophenone derivatives (2a-2u) with N,N-dimethylformamidedimethylacetal yielding enaminones (3a-3u), followed by cyclization with iodine to corresponding 3-iodochromones. Characterization of these compounds was done by IR, 1H NMR, 13C NMR, and LC-HRMS techniques. All synthesized compounds were screened for their fungicidal activity against Sclerotium rolfsii. Among these 6,8-Dichloro-3-iodochromone 4r was found to be most active (ED50 = 8.43 mg L-1). 2D-Quantitative Structural Activity Relationship (2D-QSAR) analysis was also performed by generating three different models viz., Multiple Linear Regression (MLR, Model 1), Principal Component Regression (PCR, Model 2), and Partial Least Squares (PLS, Model 3). Predictive power and statistical significance of these models were assessed with external and internal validation and leave one-out cross-validation was used for verification. In QSAR study, MLR (Model 1) was found to be best having correlation coefficient (r2) 0.943, cross-validated correlation coefficient (q2) 0.911 and r2pred 0.837. It was observed that DeltaEpsilonC, T_2_Cl_6, T_2_F_6, T_T_F_3, and ZCompDipole are the major descriptors which influence the fungicidal activity of 3-Iodochromone derivatives. The physicochemical parameters were estimated by the VLifeMDS 4.6 software. The QSAR study results will be helpful for structure optimization to improve the activity.

Computational Modeling to Explain Why 5,5-Diarylpentadienamides are TRPV1 Antagonists

Several years ago, the crystallographic structures of the transient receptor potential vanilloid 1 (TRPV1) in the presence of agonists and antagonists were reported, providing structural information about its chemical activation and inactivation. TRPV1’s activation increases the transport of calcium and sodium ions, leading to the excitation of sensory neurons and the perception of pain. On the other hand, its antagonistic inactivation has been explored to design analgesic drugs. The interactions between the antagonists 5,5-diarylpentadienamides (DPDAs) and TRPV1 were studied here to explain why they inactivate TRPV1. The present work identified the structural features of TRPV1–DPDA complexes, starting with a consideration of the orientations of the ligands inside the TRPV1 binding site by using molecular docking. After this, a chemometrics analysis was performed (i) to compare the orientations of the antagonists (by using LigRMSD), (ii) to describe the recurrent interactions between the protein residues and ligand groups in the complexes (by using interaction fingerprints), and (iii) to describe the relationship between topological features of the ligands and their differential antagonistic activities (by using a quantitative structure–activity relationship (QSAR) with 2D autocorrelation descriptors). The interactions between the DPDA groups and the residues Y511, S512, T550, R557, and E570 (with a recognized role in the binding of classic ligands), and the occupancy of isoquinoline or 3-hydroxy-3,4-dihydroquinolin-2(1H)-one groups of the DPDAs in the vanilloid pocket of TRPV1 were clearly described. Based on the results, the structural features that explain why DPDAs inactivate TRPV1 were clearly exposed. These features can be considered for the design of novel TRPV1 antagonists.

Microwave assisted synthesis, characterization and biological activities of ferrocenyl chalcones and their QSAR analysis: Part II

A series of ferrocenyl chalcones using acetylferrocene, with ferrocenyl group at the keto carbonyl group, and different aldehydes were synthesized and their bioefficacy evaluation was done against Sclerotium rolfsii, Alternaria solani and Meloidogyne incognita. In continuation of our quest for potent crop protection products, in the present study, a series of 18 substituted ferrocenyl chalcones were synthesized in which ferrocenyl group was attached to the aldehyde moiety, using ferrocenecarboxyaldehyde and different acetophenones by microwave method (MM) and conventional method (CM) [cf: MM 1 to 5 min; CM 12-40 h] and characterized by various techniques viz. IR, LC-HRMS, ¹H-NMR and ¹³C-NMR. In vitro fungicidal activity showed that compound, (2E)-1-(5-Chloro-2-hydroxyphenyl)-3-ferrocenyl-prop-2-en-1-one (34) (ED₅₀ = 21.50 mg L^-1) was found to be most active against S. rolfsii and compound, (2E)-1-(4-Bromophenyl)-3-ferrocenyl-prop-2-en-1-one (21) (ED₅₀ = 31.14 mg L^-1) showed highest activity against A. solani. As regards nematicidal activity, compound (2E)-1-(3-Bromophenyl)-3-ferrocenyl-prop-2-en-1-one (29) was more potent with LC₅₀ values of 11.95, 8.07 and 4.34 mg L^-1 at 24, 48 and 72 h, respectively. QSAR study revealed that MLR for S. rolfsii (r ² = 0.9834, q ²= 0.8975) and A. solani (r ² = 0.9807, q ²= 0.8713) and PLS for M. incognita (r ² = 0.9023, q ²= 0.7818) were the best models.

Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors

Multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) has proved to be a high-performance 2D tool for drug design purposes. Nonetheless, MIA-QSAR strategy does not efficiently incorporate conformational information. Therefore, understanding the implications of including this type of data into the MIA-QSAR model, in terms of predictability and interpretability, seems a crucial task. Conformational information was included considering the optimised geometries and the docked structures of a series of disulfide compounds potentially useful as SARS-CoV protease inhibitors. The traditional analysis (based on flat-shape molecules) proved itself as the most effective technique, which means that, despite the undeniable importance of conformation for biomolecular behaviour, this type of information did not bring relevant contributions for MIA-QSAR modelling. Consequently, promising drug candidates were proposed on the basis of MIA-plot analyses, which account for PLS regression coefficients and variable importance in projection scores of the MIA-QSAR model.

'Ideal correlations' for the predictive toxicity to Tetrahymena pyriformis

OBJECTIVES

Predictive models for toxicity to Tetrahymena pyriformis are an important component of natural sciences. The present study aims to build up a predictive model for the endpoint using the so-called index of ideality of correlation (IIC). Besides, the comparison of the predictive potential of these models with the predictive potential of models suggested in the literature is the task of the present study.

METHODS

The Monte Carlo technique is a tool to build up the predictive model applied in this study. The molecular structure is represented via a simplified molecular input-line entry system (SMILES). The IIC is a statistical characteristic sensitive to both the correlation coefficient and mean absolute error. Applying of the IIC to build up quantitative structure-activity relationships (QSARs) for the toxicity to Tetrahymena pyriformis improves the predictive potential of those models for random splits into the training set and the validation set. The calculation was carried out with CORAL software (http://www.insilico.eu/coral).

RESULTS

The statistical quality of the suggested models is incredibly good for the external validation set, but the statistical quality of the models for the training set is modest. This is the paradox of ideal correlation, which is obtained with applying the IIC.

CONCLUSIONS

The Monte Carlo technique is a convenient and reliable way to build up a predictive model for toxicity to Tetrahymena pyriformis. The IIC is a useful statistical criterion for building up predictive models as well as for the assessment of their statistical quality.

QSAR studies on benzothiophene derivatives as Plasmodium falciparum N-myristoyltransferase inhibitors: Molecular insights into affinity and selectivity

Malaria is an infectious disease caused by protozoan parasites of the genus Plasmodium and transmitted by Anopheles spp. mosquitos. Due to the emerging resistance to currently available drugs, great efforts must be invested in discovering new molecular targets and drugs. N-myristoyltransferase (NMT) is an essential enzyme to parasites and has been validated as a chemically tractable target for the discovery of new drug candidates against malaria. In this work, 2D and 3D quantitative structure-activity relationship (QSAR) studies were conducted on a series of benzothiophene derivatives as P. falciparum NMT (PfNMT) and human NMT (HsNMT) inhibitors to shed light on the molecular requirements for inhibitor affinity and selectivity. A combination of Quantitative Structure-activity Relationship (QSAR) methods, including the hologram quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) models, were used, and the impacts of the molecular alignment strategies (maximum common substructure and flexible ligand alignment) and atomic partial charge methods (Gasteiger-Hückel, MMFF94, AM1-BCC, CHELPG, and Mulliken) on the quality and reliability of the models were assessed. The best models exhibited internal consistency and could reasonably predict the inhibitory activity against both PfNMT (HQSAR: q² /r² /r² _pred = 0.83/0.98/0.81; CoMFA: q² /r² /r² _pred = 0.78/0.97/0.86; CoMSIA: q² /r² /r² _pred = 0.74/0.95/0.82) and HsNMT (HQSAR: q² /r² /r² _pred = 0.79/0.93/0.74; CoMFA: q² /r² /r² _pred = 0.82/0.98/0.60; CoMSIA: q² /r² /r² _pred = 0.62/0.95/0.56). The results enabled the identification of the polar interactions (electrostatic and hydrogen-bonding properties) as the major molecular features that affected the inhibitory activity and selectivity. These findings should be useful for the design of PfNMT inhibitors with high affinities and selectivities as antimalarial lead candidates.

Structural Requirements of N-alpha-Mercaptoacetyl Dipeptide (NAMdP) Inhibitors of Pseudomonas Aeruginosa Virulence Factor LasB: 3D-QSAR, Molecular Docking, and Interaction Fingerprint Studies

The zinc metallopeptidase Pseudomonas elastase (LasB) is a virulence factor of Pseudomonas aeruginosa (P. aeruginosa), a pathogenic bacterium that can cause nosocomial infections. The present study relates the structural analysis of 118 N-alpha-mercaptoacetyl dipeptides (NAMdPs) as LasB inhibitors. Field-based 3D-QSAR and molecular docking methods were employed to describe the essential interactions between NAMdPs and LasB binding sites, and the chemical features that determine their differential activities. We report a predictive 3D-QSAR model that was developed according to the internal and external validation tests. The best model, including steric, electrostatic, hydrogen bond donor, hydrogen bond acceptor, and hydrophobic fields, was found to depict a three-dimensional map with the local positive and negative effects of these chemotypes on the LasB inhibitory activities. Furthermore, molecular docking experiments yielded bioactive conformations of NAMdPs inside the LasB binding site. The series of NAMdPs adopted a similar orientation with respect to phosphoramidon within the LasB binding site (crystallographic reference), where the backbone atoms of NAMdPs are hydrogen-bonded to the LasB residues N112, A113, and R198, similarly to phosphoramidon. Our study also included a deep description of the residues involved in the protein-ligand interaction patterns for the whole set of NAMdPs, through the use of interaction fingerprints (IFPs).

Microwave Assisted Synthesis, Characterization and Biological Activities of Ferrocenyl Chalcones and Their QSAR Analysis

A new microwave method (MM) has been developed for the synthesis of a series of 16 substituted ferrocenyl chalcones using acetylferrocene (1) with different aldehydes (2a-2p) and comparing it with conventional method (CM). The synthesized compounds were characterized by various spectroscopic techniques viz IR, HR-MS, 1H NMR, and 13C NMR. The time required for completion of reaction in MM varied from 1 to 5 min as compared to CM which required 10-40 h. All the synthesized compounds were screened for antifungal activity against Sclerotium rolfsii and Alternaria solani. In vitro fungicidal activity revealed that compound 3o (ED50 = 23.24 mg L-1) was found to be most active against S. rolfsii. But in case of A. solani, compound 3c (ED50 = 29.9 mg L-1) showed highest activity. The nematicidal activity revealed that the compound 3b was more potent with LC50 values of 10.67, 7.30, and 4.55 ppm at 24, 48, and 72 h, respectively. 2D-Quantitative Structural Activity Relationship (2D-QSAR) analysis of these ferrocenyl chalcones was carried out by developing three different models namely Partial Least Squares (PLS, Model 1), Multiple Linear Regression (MLR, Model 2) and Principal Component Regression (PCR, Model 3). Statistical significance and predictive ability of these models were assessed by internal and external validation and also verified by leave one-out cross-validation. QSAR study revealed that MLR for S. rolfsii (r 2 = 0.999, q 2 = 0.996), PLS for A. solani (r 2 = 0.934, q 2 = 0.749) and PCR for M. incognita (r 2 = 0.878, q 2 = 0.772) were the best model. The physico-chemical parameters were calculated using VLife MDS 4.6 software. QSAR study could be employed for structure optimization to achieve better activity.

4D- quantitative structure-activity relationship modeling: making a comeback

Introduction: Predictive Quantitative Structure-Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.

Calculating the Partition Coefficients of Organic Solvents in Octanol/Water and Octanol/Air

Partition coefficients define how a solute is distributed between two immiscible phases at equilibrium. The experimental estimation of partition coefficients in a complex system can be an expensive, difficult, and time-consuming process. Here a computational strategy to predict the distributions of a set of solutes in two relevant phase equilibria is presented. The octanol/water and octanol/air partition coefficients are predicted for a group of polar solvents using density functional theory (DFT) calculations in combination with a solvation model based on density (SMD) and are in excellent agreement with experimental data. Thus, the use of quantum-chemical calculations to predict partition coefficients from free energies should be a valuable alternative for unknown solvents. The obtained results indicate that the SMD continuum model in conjunction with any of the three DFT functionals (B3LYP, M06-2X, and M11) agrees with the observed experimental values. The highest correlation to experimental data for the octanol/water partition coefficients was reached by the M11 functional; for the octanol/air partition coefficient, the M06-2X functional yielded the best performance. To the best of our knowledge, this is the first computational approach for the prediction of octanol/air partition coefficients by DFT calculations, which has remarkable accuracy and precision.

Insecticidal activity of phenolic acid amides against brown planthopper (BPH), Nilaparvata lugens (Stål) and their QSAR analysis

A series of 42 phenolic acid amides, synthesized using different phenolic acids (salicylic acid, 3-hydroxy cinnamic acid, p-coumaric acid, caffeic acid, ferulic acid, o-coumaric acid, cinnamic acid and amines (propyl amine, hexyl amine, heptyl amine, undecyl amine, hexadecyl amine, octadecyl amine) were screened for their insecticidal activities against Brown Planthopper (BPH), Nilaparvata lugens. These phenolic acid amides showed moderate to good insecticidal activity with the lowest LC₅₀ value of 63.84 ppm from N-propyl-2-hydroxycinnamamide. 2D-Quantitative structural activity relationship (2D-QSAR) analysis of these phenolic acid amides was carried out by developing three different models namely multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS). Statistical significance and predictive ability of these models were assessed by internal and external validation and verified by leave one-out cross-validation. PLS (model 3) was found best for QSAR study with correlation coefficient (r²) 0.8388, cross-validated correlation coefficient (q²) 0.7797 and r² pred 0.7347. It was found that + vePotentialSurfaceArea, XAMostHydrophobic, SaasCE-index, T_O_O_3 and T_O_O_6 are the major descriptors which influence the insecticidal activities of these phenolic acid amides. The QSAR study could help in structural optimization of phenolic acid amides in developing potential compounds to get better bioefficacy against N. lugens.

Probability-driven 3D pharmacophore mapping of antimycobacterial potential of hybrid molecules combining phenylcarbamoyloxy and N-arylpiperazine fragments

The current study examines in silico characterization of the structure-inhibitory potency for a set of phenylcarbamic acid derivatives containing an N-arylpiperazine scaffold, considering the electronic, steric and lipophilic properties. The main objective of the ligand-based modelling was the systematic study of classical comparative molecular field analysis (CoMFA)/comparative molecular surface analysis (CoMSA) performance for the modelling of in vitro efficiency observed for these phenylcarbamates, revealing their inhibitory activities against a virulent Mycobacterium tuberculosis H₃₇R_v strain. We compared the findings of efficiency modelling produced by a standard 3D methodology (CoMFA) and its neural counterparts (CoMSA) regarding multiple training/test subsets and variables used. Moreover, systematic space inspection, splitting values into the analysed training/test subsets, was performed to monitor statistical estimator performance while mapping the probability-driven pharmacophore pattern. Consequently, a 'pseudo-consensus' 3D-quantitative structure-activity relationship (3D-QSAR) approach was applied to retrieve an 'average' pharmacophore hypothesis by the investigation of the most densely populated training/test subpopulations to specify the potentially important factors contributing to the inhibitory activity of phenylcarbamic acid analogues. In addition, examination of descriptor-based similarity with a principal component analysis (PCA) procedure was employed to visualize noticeable variations in the performance of these molecules with respect to their structure and activity profiles.

Modelling methods and cross-validation variants in QSAR: a multi-level analysis$

Prediction performance often depends on the cross- and test validation protocols applied. Several combinations of different cross-validation variants and model-building techniques were used to reveal their complexity. Two case studies (acute toxicity data) were examined, applying five-fold cross-validation (with random, contiguous and Venetian blind forms) and leave-one-out cross-validation (CV). External test sets showed the effects and differences between the validation protocols. The models were generated with multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS) regression, artificial neural networks (ANN) and support vector machines (SVM). The comparisons were made by the sum of ranking differences (SRD) and factorial analysis of variance (ANOVA). The largest bias and variance could be assigned to the MLR method and contiguous block cross-validation. SRD can provide a unique and unambiguous ranking of methods and CV variants. Venetian blind cross-validation is a promising tool. The generated models were also compared based on their basic performance parameters (r² and Q²). MLR produced the largest gap, while PCR gave the smallest. Although PCR is the best validated and balanced technique, SVM always outperformed the other methods, when experimental values were the benchmark. Variable selection was advantageous, and the modelling had a larger influence than CV variants.

Theoretical Model of EphA2-Ephrin A1 Inhibition

This work aims at the theoretical description of EphA2-ephrin A1 inhibition by small molecules. Recently proposed ab initio-based scoring models, comprising long-range components of interaction energy, is tested on lithocholic acid class inhibitors of this protein–protein interaction (PPI) against common empirical descriptors. We show that, although limited to compounds with similar solvation energy, the ab initio model is able to rank the set of selected inhibitors more effectively than empirical scoring functions, aiding the design of novel compounds.

Molecular description of α-keto-based inhibitors of cruzain with activity against Chagas disease combining 3D-QSAR studies and molecular dynamics

In this work, a group of α-keto-based inhibitors of the cruzain enzyme with anti-chagas activity was selected for a three-dimensional quantitative structure-activity relationship study (3D-QSAR) combined with molecular dynamics (MD). Firstly, statistical models based on Partial Least Square (PLS) regression were developed employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) descriptors. Validation parameters (q² and r² )for the models were, respectively, 0.910 and 0.997 (CoMFA) and 0.913 and 0.992 (CoMSIA). In addition, external validation for the models using a test group revealed r²_pred  = 0.728 (CoMFA) and 0.971 (CoMSIA). The most relevant aspect in this study was the generation of molecular fields in both favorable and unfavorable regions based on the models developed. These fields are important to interpret modifications necessary to enhance the biological activities of the inhibitors. This analysis was restricted considering the inhibitors in a fixed conformation, not interacting with their target, the cruzain enzyme. Then, MD was employed taking into account important variables such as time and temperature. MD helped describe the behavior of the inhibitors and their properties showed similar results as those generated by QSAR-3D study.

QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium, affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of Plasmodium falciparum (PfdUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei. We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as PfdUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual PfdUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity.

Application of a simple quantum chemical approach to ligand fragment scoring for Trypanosoma brucei pteridine reductase 1 inhibition

There is a need for improved and generally applicable scoring functions for fragment-based approaches to ligand design. Here, we evaluate the performance of a computationally efficient model for inhibitory activity estimation, which is composed only of multipole electrostatic energy and dispersion energy terms that approximate long-range ab initio quantum mechanical interaction energies. We find that computed energies correlate well with inhibitory activity for a compound series with varying substituents targeting two subpockets of the binding site of Trypanosoma brucei pteridine reductase 1. For one subpocket, we find that the model is more predictive for inhibitory activity than the ab initio interaction energy calculated at the MP2 level. Furthermore, the model is found to outperform a commonly used empirical scoring method. Finally, we show that the results for the two subpockets can be combined, which suggests that this simple nonempirical scoring function could be applied in fragment–based drug design.

QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni

Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents.

Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA

Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure-activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes.

Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches

Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligands bound, structure-based approaches have been successfully applied to AChE inhibitors (AChEIs). The major limitation of these approaches has been the small applicability domain due to the lack of structural diversity in the training set. In this study, we developed a 3 dimensional quantitative structure-activity relationship (3D-QSAR) for inhibitory activity of 89 reversible and irreversible AChEIs including drugs and insecticides. A 3D-fingerprint descriptor encoding protein-ligand interactions was developed using molecular docking and structure-based pharmacophore to rationalize the structural requirements responsible for the activity of these compounds. The obtained 3D-QSAR model exhibited high correlation value (R(2) = 0.93) and low mean absolute error (MAE = 0.32 log units) for the training set (n = 63). The model was predictive across a range of structures as shown by the leave-one-out cross-validated correlation coefficient (Q(2) = 0.89) and external validation results (n = 26, R(2) = 0.89, and MAE = 0.38 log units). The model revealed that the compounds with high inhibition potency had proper conformation in the active site gorge and interacted with key amino acid residues, in particular Trp84 and Phe330 at the catalytic anionic site, Trp279 at the peripheral anionic site, and Gly118, Gly119, and Ala201 at the oxyanion hole. The resulting universal 3D-QSAR model provides insight into the multiple molecular interactions determining AChEI potency that may guide future chemical design and regulation of toxic AChEIs.

Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q(2)>0.4, r(2)>0.5 and r(2)pred>0.5. Based on better q(2) and r(2)pred values, the best predictions were obtained for the CoMFA (model 5 q(2)=0.488, r(2)pred=0.732), and CoMSIA (model 45 q(2)=0.525, r(2)pred=0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Challenging the gold standard for 3D-QSAR: template CoMFA versus X-ray alignment

X-ray-based alignments of bioactive compounds are commonly used to correlate structural changes with changes in potencies, ultimately leading to three-dimensional quantitative structure-activity relationships such as CoMFA or CoMSIA models that can provide further guidance for the design of new compounds. We have analyzed data sets where the alignment of the compounds is entirely based on experimentally derived ligand poses from X-ray-crystallography. We developed CoMFA and CoMSIA models from these X-ray-determined receptor-bound conformations and compared the results with models generated from ligand-centric Template CoMFA, finding that the fluctuations in the positions and conformations of compounds dominate X-ray-based alignments can yield poorer predictions than those from the self-consistent template CoMFA alignments. Also, when there exist multiple different binding modes, structural interpretation in terms of binding site constraints can often be simpler with template-based alignments than with X-ray-based alignments.

X-ray crystallographic structures as a source of ligand alignment in 3D-QSAR

Three-dimensional quantitative structure-activity relationships (3D-QSAR) analyses are methods correlating a pharmacological property with a mathematical representation of a molecular property distribution around three-dimensional molecular models for a set of congeners. 3D-QSAR methods are known to be highly sensitive to ligand conformation and alignment method. The current study collects 32 unique positions of congeneric ligands co-crystallized with the binding domain of AMPA receptors and aligns them using protein coordinates. Thus, it allows for a unique opportunity to consider a ligands' orientation aligned by their mode of binding in a native molecular target. Comparative molecular field analysis (CoMFA) models were generated for this alignment and compared with the results of analogous modeling using standard structure-based alignment or obtained in docking simulations of the ligands' molecules. In comparison with classically derived models, the model based on X-ray crystallographic studies showed much better performance and statistical significance. Although the 3D-QSAR methods are mainly employed when crystallographic information is limited, the current study underscores the importance that the selection of inappropriate molecular conformations and alignment methods can lead to generation of erroneous models and false conclusions.

Identification of electronic and structural descriptors of adenosine analogues related to inhibition of leishmanial glyceraldehyde-3-phosphate dehydrogenase

Quantitative structure-activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4. Variable selection was undertaken with the ordered predictor selection (OPS) algorithm, which provided a set with the most relevant descriptors to perform PLS, PCR and MLR regressions. Reliable and predictive models were obtained, as attested by their high correlation coefficients, as well as the agreement between predicted and experimental values for an external test set. Additional validation procedures were carried out, demonstrating that robust models were developed, providing helpful tools for the optimization of the antileishmanial activity of adenosine compounds.

Exploring novel KDR inhibitors based on pharmaco-informatics methodology

Kinase-insert domain-containing receptor (KDR) is one of the important mediators of Vascular endothelial growth factor (VEGF) function in endothelial cells. Inhibition of KDR can be therapeutically advantageous for treatment of a number of diseases. The present study focuses on exploring novel KDR inhibitors by means of pharmaco-informatics methodologies. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis by atom-based pharmacophore mapping over a set of 85 molecules provides a proposition regarding the molecular fingerprint that can be optimized for designing more active inhibitors. The model was statistically validated with Q(2) = 0.865 for training and r(2) = 0.789, Pearson-r = 0.903 for test set molecules; r(2)(0.925) by external validation suggests model robustness and indicates it as a strong query for screening any compound library. Virtual screening shows the importance of active site and hinge region residue for interaction with KDR inhibitors. Remarkably the retrieved hits contain a urea backbone, implicating urea derivatives as promising candidate for designing KDR inhibitors. The hydrophobicity of active site, which has until now been overlooked, has been raised into the picture by this study. This can impact on KDR drug development. The study thus quantifies crucial structural requirements necessary for a favourable interaction with the receptor binding site while the cooperative pattern provides important structural clues to chemists for framing potent medicinal agents in future.

Analysis of Ah receptor binding affinities of polybrominated diphenyl ethers via in silico molecular docking and 3D-QSAR

Polybrominated diphenyl ethers (PBDEs) have become ubiquitous contaminations due to their use as flame retardants. The structural similarity of PBDE to some dioxin-like compounds suggested that they may share similar toxicological effects: they might activate the aryl hydrocarbon receptor (AhR) signal transduction pathway and thus might have adverse effects on wildlife and humans. In this study, in silico computational workflow combining molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) was performed to investigate the binding interactions between PBDEs and AhR and the structural features affecting the AhR binding affinity of PBDE. The molecular docking showed that hydrogen-bond and hydrophobic interactions were the major driving forces for the binding of ligands to AhR, and several key amino acid residues were also identified. The CoMSIA model was developed from the conformations obtained from molecular docking and exhibited satisfactory results as q (2) of 0.605 and r (2) of 0.996. Furthermore, the derived model had good robustness and statistical significance in both internal and external validations. The 3D contour maps generated from CoMSIA provided important structural features influence the binding affinity. The obtained results were beneficial to better understand the toxicological mechanism of PBDEs.

Direct calculation of 1-octanol-water partition coefficients from adaptive biasing force molecular dynamics simulations

The 1-octanol-water partition coefficient log K(ow) of a solute is a key parameter used in the prediction of a wide variety of complex phenomena such as drug availability and bioaccumulation potential of trace contaminants. In this work, adaptive biasing force molecular dynamics simulations are used to determine absolute free energies of hydration, solvation, and 1-octanol-water partition coefficients for n-alkanes from methane to octane. Two approaches are evaluated; the direct transfer of the solute from 1-octanol to water phase, and separate transfers of the solute from the water or 1-octanol phase to vacuum, with both methods yielding statistically indistinguishable results. Calculations performed with the TIP4P and SPC∕E water models and the TraPPE united-atom force field for n-alkanes show that the choice of water model has a negligible effect on predicted free energies of transfer and partition coefficients for n-alkanes. A comparison of calculations using wet and dry octanol phases shows that the predictions for log K(ow) using wet octanol are 0.2-0.4 log units lower than for dry octanol, although this is within the statistical uncertainty of the calculation.

3D-QSAR studies of triazolopyrimidine derivatives of Plasmodium falciparum dihydroorotate dehydrogenase inhibitors using a combination of molecular dynamics, docking, and genetic algorithm-based methods

A series of 35 triazolopyrimidine analogues reported as Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors were optimized using quantum mechanics methods, and their binding conformations were studied by docking and 3D quantitative structure-activity relationship studies. Genetic algorithm-based criteria was adopted for selection of training and test sets while maintaining structural diversity of training and test sets, which is also very crucial for model development and validation. Both the comparative molecular field analyses ([Formula: see text], [Formula: see text]) and comparative molecular similarity indices analyses ([Formula: see text], [Formula: see text]) show excellent correlation and high predictive power. Furthermore, molecular dynamics simulations were performed to explore the binding mode of the two of the most active compounds of the series, 10 and 14. Harmonization in the two simulation results validate the analysis and therefore applicability of docking parameters based on crystallographic conformation of compound 14 bound to receptor molecule. This work provides useful information about the inhibition mechanism of this class of molecules and will assist in the design of more potent inhibitors of PfDHODH.

R-group template CoMFA combines benefits of "ad hoc" and topomer alignments using 3D-QSAR for lead optimization

Template CoMFA methodologies extend topomer CoMFA by allowing user-designated templates, for example the experimental receptor-bound conformation of a prototypical ligand, to help determine the alignment of training and test set structures for 3D-QSAR. The algorithms that generate its new structural modality, template-constrained topomers, are described. Template CoMFA's resolution of certain topomer CoMFA concerns, by providing user control of topological consistency and structural acceptability, is demonstrated for sixteen 3D-QSAR training sets, in particular the Selwood dataset.

Exploring the substructural space of indole-3-carboxamide derivatives binding to renin: a novel active-site spatial partitioning approach

Renin has recently attracted much attention in the antihypertensive community, since this enzyme starts the angiotensin-converting cascade and forms the rate-limiting step in this cascade. In the present study, we describe a new method called active-site spatial partitioning (ASSP) for quantitatively characterizing the nonbonding interaction profile between renin and the substructures of indole-3-carboxamide derivatives-a novel class of achiral renin inhibitors that exhibit both high affinity and strong specificity for renin, thus blocking its active state-on the basis of structural models of protein-ligand complexes. It is shown that the ASSP-derived potential parameters are highly correlated with the experimentally measured activities of indole-3-carboxamides; the statistical models linking the parameters and activities using a sophisticated partial least squares regression technique show much promise as an effective and powerful tool for generalizing and predicting the pharmaceutical potencies and the physicochemical properties of other modified derivatives. Furthermore, by visually examining substructure-color plots generated by the ASSP procedure, it is found that the relative importance of nonbonding contributions to the recognition and binding of a ligand by renin is as follows: steric < hydrophobic < electrostatic. The polar and charged moieties that float on the surface of the ligand molecule play a critical role in conferring electrostatic stability and specificity to renin-ligand complexes, whereas the aromatic rings embedded in the core region of the ligand are the main source of hydrophobic and steric potentials that lead to substantial stabilization of the complex architecture.

The inevitable QSAR renaissance

QSAR approaches, including recent advances in 3D-QSAR, are advantageous during the lead optimization phase of drug discovery and complementary with bioinformatics and growing data accessibility. Hints for future QSAR practitioners are also offered.