Modelling the cytotoxic activity of pyrazolo-triazole hybrids using descriptors calculated from the open source tool “PaDEL-descriptor”

Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics

Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.Communicated by Ramaswamy H. Sarma.

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

Discovery of potential anti- Staphylococcus aureus natural products and their mechanistic studies using machine learning and molecular dynamic simulations

The structure-activity analysis (SAR) and machine learning were used to investigate potential anti-S. aureus agents in a faster method. In this study, 24 oxygenated benzene ring components with S. aureus inhibition capacity were confirmed by literature exploring and in-house experiments, and the SAR analysis suggested that the hydroxyl group position may affect the anti-S. aureus activity. The 2D-MLR-QSAR model with 9 descriptors was further evaluated as the best model among the 21 models. After that, hesperetic acid and 2-HTPA were further explored and evaluated as the potential anti-S. aureus agents screening in the natural product clustering library through the best QSAR model calculation. The antibacterial capacities of hesperetic acid and 2-HTPA had been investigated and proved the similar predictive pMIC value resulting from the QSAR model. Besides, the two novel components were able to inhibit the growth of S. aureus by disrupting the cell membrane through the molecular dynamics simulation (MD), which further evidenced by scanning electron microscopy (SEM) test and PI dye results. Overall, these results are highly suggested that QSAR can be used to predict the antibacterial agents targeting S. aureus, which provides a new paradigm to research the molecular structure-antibacterial capacity relationship.

Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis

Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facilitate the identification of unknown compounds, notably with non-targeted analysis. In this study, QSRR models were developed from the data obtained for 178 pure chemical standards and four types of analytical columns (C18, phenylhexyl, pentafluorophenyl, cyano) in liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, and RF models resulted in better predictive capacities [p < 0.05, with an average PVE (proportion of variance explained) value of 0.89 ± 0.02] than linear models (0.79 ± 0.03). For each column, the resulting model was applied to identify leachables from actual plastic packaging samples. An in-depth investigation of the top 20 most intense molecular features revealed that all false-positives could be identified as outliers in the QSRR models (outside of the 95% prediction bands). Furthermore, analyzing a sample on multiple chromatographic columns and applying the associated QSRR models increased the capacity to filter false positives. Such an approach will contribute to a more effective identification of unknown or unexpected leachables in plastics (e.g. non-intended added substances), therefore refining our understanding of the chemical risks associated with food contact materials.

Chemometric modeling of plant protection products (PPPs) for the prediction of acute contact toxicity against honey bees (A. mellifera): A 2D-QSAR approach

Honey bees (Apis mellifera) are vital for economic, viable agriculture and for food safety. Although Plant Protection Products (PPPs) are of undeniable importance in the global agricultural system, these have become potential threats for non-target organisms like pollinators (e.g., honey bees etc.), resulting in the disruption of the ecological balance. In the current work, we have used the 113 PPP analogs to develop a 2D-QSAR model and explored the structural features modulating the toxic effects on honey bees, following the Organization for Economic Co-operation and Development (OECD) guidelines. The extensive validation of the developed model has been performed using internal and external validation metrics to make sure that the model is statistically sound and interpretable enough to be acceptable. The obtained results (R² = 0.666, Q² = 0.594, Q²_F1 = 0.647 and Q²_F2 = 0.646) determine the predictability and reliability of the developed model. This model should be useful for the predictions (acute contact toxicity (LD₅₀)) of the new and untested compounds located inside the applicability domain of the developed model. Moreover, we have performed the in-silico prediction of toxicity against honey bees of a total of 709 compounds obtained from the pesticide properties database (PPDB) using the developed model.

Antiprotozoal QSAR modelling for trypanosomiasis (Chagas disease) based on thiosemicarbazone and thiazole derivatives

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, remains a neglected endemic infection that affects around 8 million people worldwide and causes 12,000 premature deaths per year. Traditional chemotherapy is limited to the nitro-antiparasitic drugs Benznidazole and Nifurtimox, which present serious side effects and low long-term efficacy. Several research efforts have been made over the last decade to find new chemical structures with better effectiveness and tolerance than standard anti-Chagas drugs. Among these, new sets of thiosemicarbazone and thiazole derivatives have exhibited potent in vitro activity against T. cruzi, especially for its extracellular forms (epimastigote and trypomastigote). In this work, we have developed three antiprotozoal quantitative structure-relationship (QSAR) models for Chagas disease based on the in vitro activity data reported as IC₅₀ (μM) and CC₅₀ (μM) over the last decade, particularly by Lima-Leite's group in Brazil. The models were developed using the replacement method (RM), a technique based on Multivariable Linear Regression (MLR), and external and internal validation methodologies, like the use of a test set, Leave-one-Out (LOO) cross-validation and Y-Randomization. Two of these QSAR models were developed for trypomastigotes form of the parasite Trypanosoma cruzi, one based on IC₅₀ and the other on CC₅₀ data; while the third QSAR model was developed for its epimastigotes form based on CC₅₀ activity. Our models presented sound statistical parameters that endorses their prediction capability. Such capability was tested for a set of 13 hitherto-unknown structurally related aromatic cyclohexanone derivatives.

An integrated QSAR modeling approach to explore the structure-property and selectivity relationships of N-benzoyl-L-biphenylalanines as integrin antagonists

Integrins [Formula: see text] and [Formula: see text] are important targets to treat different inflammatory diseases, such as multiple sclerosis, inflammatory bowel diseases, rheumatoid arthritis, atherosclerosis, and asthma. Despite being valuable targets, only a few work has been reported to date regarding molecular modeling studies on these integrins. Not only that, none of these reports addressed the selectivity issue between integrins [Formula: see text] and [Formula: see text]. Therefore, a major challenge regarding the design and discovery of selective integrin antagonists remains. In this study, a series of 142 N-benzoyl-L-biphenylalanines having both integrin [Formula: see text] and [Formula: see text] inhibitory activities were considered for a variety of QSAR approaches including regression and classification-based 2D-QSARs, Hologram QSARs, 3D-QSAR CoMFA and CoMSIA studies to identify the structural requirements of these integrin antagonists. All these QSAR models were statistically validated and subsequently correlated with each other to get a detailed understanding of the activity and selectivity profiles of these molecules.

Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches

Schizophrenia is a complex disorder of thinking and behaviour (0.3-0.7% of the population is affected). The over-expression of phosphodiesterase 10A (PDE10A) enzyme may be a potential target for schizophrenia and Huntington's disease. Because 3D QSAR analysis is one of the most frequently used modelling techniques, in the present study, five different 3D QSAR tools, namely CoMFA, CoMSIA, kNN-MFA, Open3DQSAR and topomer CoMFA methods, were used on a dataset of pyrimidine-based PDE10A inhibitors. All developed models were validated internally and externally. The non-commercial Open3DQSAR produced the best statistical results amongst 3D QSAR tools. The structural interpretations obtained from different methods were thoroughly analysed and were justified on the basis of information obtained from the crystal structure. Information from one method was mostly validated by the results of other methods and vice versa. In the current work, the use of multiple tools in the same analysis revealed more complete information about the structural requirements of these compounds. On the basis of the observations of the 3D QSAR studies, 12 new compounds were designed for better PDE10A inhibitory activity. The current investigation may help in further designing new PDE10A inhibitors with promising activity.

First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington's disease: A proposal to chemists!

Huntington's disease (HD) is caused by mutation of huntingtin protein (mHtt) leading to neuronal cell death. The mHtt induced toxicity can be rescued by inhibiting the kynurenine monooxygenase (KMO) enzyme. Therefore, KMO is a promising drug target to address the neurodegenerative disorders such as Huntington's diseases. Fiftysix arylpyrimidine KMO inhibitors are structurally explored through regression and classification based multi-QSAR modeling, pharmacophore mapping and molecular docking approaches. Moreover, ten new compounds are proposed and validated through the modeling that may be effective in accelerating Huntington's disease drug discovery efforts.