p<Emphasis Type="Italic">K</Emphasis><Subscript>a</Subscript> measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transfer to another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pK_a) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pK_a prediction component to assess the accuracy with which contemporary pK_a prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pK_a values currently exist, predicting the pK_as of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors—an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid–base titrations, we used UV absorbance-based pK_a measurements to construct a high-quality experimental reference dataset of macroscopic pK_as for the evaluation of computational pK_a prediction methodologies that was utilized in the SAMPL6 pK_a challenge. For several compounds in which the microscopic protonation states associated with macroscopic pK_as were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pK_a prediction methodologies on kinase inhibitor-like compounds.     

SAMPL6: calculation of macroscopic p<Emphasis Type="Italic">K</Emphasis><Subscript>a</Subscript> values from ab initio quantum mechanical free energies

Macroscopic pK_a values were calculated for all compounds in the SAMPL6 blind prediction challenge, based on quantum chemical calculations with a continuum solvation model and a linear correction derived from a small training set. Microscopic pK_a values were derived from the gas-phase free energy difference between protonated and deprotonated forms together with the Conductor-like Polarizable Continuum Solvation Model and the experimental solvation free energy of the proton. pH-dependent microstate free energies were obtained from the microscopic pK_as with a maximum likelihood estimator and appropriately summed to yield macroscopic pK_a values or microstate populations as function of pH. We assessed the accuracy of three approaches to calculate the microscopic pK_as: direct use of the quantum mechanical free energy differences and correction of the direct values for short-comings in the QM solvation model with two different linear models that we independently derived from a small training set of 38 compounds with known pK_a. The predictions that were corrected with the linear models had much better accuracy [root-mean-square error (RMSE) 2.04 and 1.95 pK_a units] than the direct calculation (RMSE 3.74). Statistical measures indicate that some systematic errors remain, likely due to differences in the SAMPL6 data set and the small training set with respect to their interactions with water. Overall, the current approach provides a viable physics-based route to estimate macroscopic pK_a values for novel compounds with reasonable accuracy.     

Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA

The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acc_{train-diverse} = 84%; acc_{internal-test} = 92% and acc_{external-test} = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.     

Development of QSAR models for microsomal stability: identification of good and bad structural features for rat,human and mouse microsomal stability

High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Naïve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.     

Characterization of <Emphasis Type="Italic">Pf</Emphasis>TrxR inhibitors using antimalarial assays and <Emphasis Type="Italic">in silico</Emphasis>techniques

Background

The compounds 1,4-napthoquinone (1,4-NQ), bis-(2,4-dinitrophenyl)sulfide (2,4-DNPS), 4-nitrobenzothiadiazole (4-NBT), 3-dimethylaminopropiophenone (3-DAP) and menadione (MD) were tested for antimalarial activity against both chloroquine (CQ)-sensitive (D6) and chloroquine (CQ)-resistant (W2) strains of Plasmodium falciparum through an in vitro assay and also for analysis of non-covalent interactions with P. falciparum thioredoxin reductase (PfTrxR) through in silico docking studies.

Results

The inhibitors of PfTrxR namely, 1,4-NQ, 4-NBT and MD displayed significant antimalarial activity with IC₅₀ values of?<?20 μM and toxicity against 3T3 cell line. 2,4-DNPS was only moderately active. In silico docking analysis of these compounds with PfTrxR revealed that 2,4-DNPS, 4-NBT and MD interact non-covalently with the intersubunit region of the enzyme.

Conclusions

In this study, tools for the identification of PfTrxR inhibitors using phenotyphic screening and docking studies have been validated for their potential use for antimalarial drug discovery project.

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An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts

Background

Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%; this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types of assessment methods, such as in silico structure-activity prediction models.A widely used method checks for the presence of known structural alerts for mutagenicity. However the presence of such alerts alone is not a definitive method to prove the mutagenicity of a compound towards Salmonella, since other parts of the molecule can influence and potentially change the classification. Hence statistically based methods will be proposed, with the final objective to obtain a cascade of modeling steps with custom-made properties, such as the reduction of false negatives.

Results

A cascade model has been developed and validated on a large public set of molecular structures and their associated Salmonella mutagenicity outcome. The first step consists in the derivation of a statistical model and mutagenicity prediction, followed by further checks for specific structural alerts in the "safe" subset of the prediction outcome space. In terms of accuracy (i.e., overall correct predictions of both negative and positives), the obtained model approached the 85% reproducibility of the experimental mutagenicity Ames test.

Conclusions

The model and the documentation for regulatory purposes are freely available on the CAESAR website. The input is simply a file of molecular structures and the output is the classification result.

     

Novel inhibitors to <Emphasis Type="Italic">Taenia solium</Emphasis> Cu/Zn superoxide dismutase identified by virtual screening

We describe in this work a successful virtual screening and experimental testing aimed to the identification of novel inhibitors of superoxide dismutase of the worm Taenia solium (TsCu/Zn–SOD), a human parasite. Conformers from LeadQuest^® database of drug-like compounds were selected and then docked on the surface of TsCu/Zn–SOD. Results were screened looking for ligand contacts with receptor side-chains not conserved in the human homologue, with a subsequent development of a score optimization by a set of energy minimization steps, aimed to identify lead compounds for in vitro experiments. Six out of fifty experimentally tested compounds showed μM inhibitory activity toward TsCu/Zn–SOD. Two of them showed species selectivity since did not inhibit the homologous human enzyme when assayed in vitro.     

Pharmacophore interactions analysis and prediction of inhibitory activity of 1,7-diazacarbazoles as checkpoint kinase 1 inhibitors: application of molecular docking, 3D-QSAR and RBF neural network

In the present study, we mainly focused on new synthesized 1,7-diazacarbazole derivatives (44 active molecules) as Chk1 inhibitors to build 3D-QSAR model. Comparative molecular field analysis (CoMFA) model with three principal components was developed. The relative contributions in building of CoMFA model were 64.41 % for steric field and 35.59 % for electrostatic field. R ² values for training and test sets of CoMFA model were 0.8724 and 0.7818, respectively, and squared correlation coefficient for leave-one-out cross-validation test (q ²) was 0.6753. To improve the predictive power, a new 3D-QSAR model was developed by using radial basis function network (RBFN) and score of CoMFA interactions energy values as input variables. Scores 1, 2 and 3 were used as input variables, and a RBFN model with seven centers and spread value equal to 95 was developed to create a nonlinear 3D-QSAR model. R ² values for training and test sets were 0.9613 and 0.8564, and q ² for leave-one-out cross-validation test was 0.9258. Docking of all molecules to 3DX ligand binding site of Chk1 receptor indicated six interactions as pharmacological interactions between compounds and binding site of receptors. These pharmacological interactions were hydrogen bonding with LEU-15 and GLU-85 in main chain and four van der Waals interactions with LEU-15, VAL-23, TYR-86 and LEU-137 in side chain. CoMFA contour plots were used to design new inhibitors, and inhibitory activity of each compound was predicted by using CoMFA and RBFN models.     

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein–ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein–ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.     

Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies

HIV-1 integrase (IN) is a retroviral enzyme that catalyses integration of the reverse-transcribed viral DNA into the host genome, which is necessary for efficient viral replication. In this study, we have performed an in silico virtual screening for the identification of potential HIV-1 IN strand transfer (ST) inhibitors. Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to 3-Hydroxypyrimidine-2,4-diones. Based on the ligand-based pharmacophore model, we obtained a five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features. The pharmacophore hypothesis AADHR was used as a 3D query in a sequential virtual screening study to filter small molecule databases Maybridge, ChemBridge and Asinex. Hits matching with pharmacophore hypothesis AADHR were retrieved and passed progressively through Lipinski’s rule of five filtering, molecular docking and hierarchical clustering. The five compounds with best hits with novel and diverse chemotypes were subjected to QM/MM docking, which showed improved docking accuracy. We further performed molecular dynamics simulation and found three compounds that form stable interactions with key residues. These compounds could be used as a leads for further drug development and rational design of HIV-1 IN inhibitors.     

DG-AMMOS: A New tool to generate 3D conformation of small molecules using Distance Geometry and Automated Molecular Mechanics Optimization for in silico Screening

Background  

Discovery of new bioactive molecules that could enter drug discovery programs or that could serve as chemical probes is a very complex and costly endeavor. Structure-based and ligand-based in silico screening approaches are nowadays extensively used to complement experimental screening approaches in order to increase the effectiveness of the process and facilitating the screening of thousands or millions of small molecules against a biomolecular target. Both in silico screening methods require as input a suitable chemical compound collection and most often the 3D structure of the small molecules has to be generated since compounds are usually delivered in 1D SMILES, CANSMILES or in 2D SDF formats.     

A machine learning-based method to improve docking scoring functions and its application to drug repurposing

Docking scoring functions are notoriously weak predictors of binding affinity. They typically assign a common set of weights to the individual energy terms that contribute to the overall energy score; however, these weights should be gene family dependent. In addition, they incorrectly assume that individual interactions contribute toward the total binding affinity in an additive manner. In reality, noncovalent interactions often depend on one another in a nonlinear manner. In this paper, we show how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinities and those predicted by the docking program eHiTS. We construct two prediction models: a regression model trained using IC(50) values from BindingDB, and a classification model trained using active and decoy compounds from the Directory of Useful Decoys (DUD). Moreover, to address the issue of overrepresentation of negative data in high-throughput screening data sets, we have designed a multiple-planar SVM training procedure for the classification model. The increased performance that both SVMs give when compared with the original eHiTS scoring function highlights the potential for using nonlinear methods when deriving overall energy scores from their individual components. We apply the above methodology to train a new scoring function for direct inhibitors of Mycobacterium tuberculosis (M.tb) InhA. By combining ligand binding site comparison with the new scoring function, we propose that phosphodiesterase inhibitors can potentially be repurposed to target M.tb InhA. Our methodology may be applied to other gene families for which target structures and activity data are available, as demonstrated in the work presented here.     

Synthesis,X-ray structure and theoretical study of benzazole thioether and its zinc complex as corrosion inhibitors for steel in acidic medium

A ligand, 2-((benzo[d]thiazol-2-ylthio)methyl)-1H-benzo[d]imidazole, and its zinc complex have been synthesized. The structure of these compounds have been determined by spectroscopic techniques and single crystal X-ray diffraction. The corrosion inhibition study of these compounds for steel in 0.5 M H₂SO₄ medium has also been investigated using potentiodynamic polarization and EIS techniques. The quantum calculations were applied to investigate the relationship between the electronic properties and the corrosion inhibition efficiency of the two benzazoles derivatives. Surface analysis (XRF) indicated that the rust layer formed on the Cu-containing steels was enriched with Cu compounds. Polarization curves revealed that both inhibitors acted as a mixed-type inhibitor.     

Microwave promoted synthesis and anticological screening of <Emphasis Type="Italic">β</Emphasis>-aminobisphosphonates-based benzothiazole motif against human breast and colon cancer diseases

New antineoplastic series of substituted benzothiazolo-β-aminobisphosphonic acids have been developed on the basis of the prospecting potency of the benzothiazole motifs and the aminomethylenebisphosphonate moiety as well as on the prediction of the biological activity utilizing computer program PASS, version 2014-1. Target compounds were obtained in excellent yields (70–90%) via Phospha–Michael-type addition reaction of tetraethyl methylene-1,1-bisphosphonate reagent to a group of Schiff bases incorporating benzothiazole moiety. The reactions proceeded under microwave irradiation, utilizing the advantages of the environment, friendly protocol such as high efficiency, short reaction time, and excellent yields. In consistency with the prospected results, the new NBP acids revealed positive properties against human breast and colon tumor cell lines. Remarkable potency for six lead compounds (out of 12) was observed against breast (MCF7, MDAMB/435, MDAMB/231/ATCC, HS-578T with GI₅₀: 2.05–6.47 μM) and colon (COLO-205, HCT-116, HCC-2998, and SW-620 with GI₅₀: 3.03–7.92 μM) carcinoma cell lines when compared with the positive control Adriamycin (breast, GI₅₀: 3.27–6.64 μM; colon, GI₅₀: 4.09–8.75 μM). Notably, there is a consistency between the prediction and the determined biological results.     

D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand–protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand–protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.     

Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS

Performance of COSMO-RS method as a tool for partition and distribution modeling in 20 solvent pairs—composed of neutral or acidic aqueous solution and organic solvents of different polarity, ranging from alcohols to toluene and hexane—was evaluated. Experimental partition/distribution data of lignin-related and drug-like compounds (neutral, acidic, moderately basic) were used as reference. Several aspects of partition modeling were addressed: accounting for mutual saturation of aqueous and organic phases, variability of systematic prediction errors across solvent pairs, taking solute ionization into account. COSMO-RS was found to predict extraction outcome for both ligneous and drug-like compounds in various solvent pairs fairly well without any additional empirical input. The solvent-specific systematic errors were found to be moderate, despite being statistically significant, and related to the solvent hydrophobicity. Accounting for mutual solubilities of the two liquids was proven crucial in cases where water was considerably soluble in the organic solvent. The root mean square error of a priori logP prediction varied, depending mainly on the solvent pair, from 0.2 to 0.7, overall value being 0.6 log units. The accuracy was higher in case of hydrophilic than hydrophobic solvents. The logD predictions were less accurate, due to pK_a prediction being an additional source of error, and also because of the complexity of modeling the behaviour of ionic species in the two-phase system. A simple correction for partitioning of free ions was found to notably improve logD prediction accuracy in case of the most hydrophilic organic phase (butanol/water).     

A <Emphasis Type="Italic">k</Emphasis>-nearest neighbor classification of hERG K<Superscript>+</Superscript> channel blockers

A series of 172 molecular structures that block the hERG K⁺ channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.