Developed QSAR Models Research Articles

Exploring molecular fragments for fraction unbound in human plasma of chemicals: a fragment-based cheminformatics approach

ABSTRACT Fraction unbound in plasma (f u,p) of drugs is an significant factor for drug delivery and other biological incidences related to the pharmacokinetic behaviours of drugs. Exploration of different molecular fragments for f u,p of different small molecules/agents can facilitate in identification of suitable candidates in the preliminary stage of drug discovery. Different researchers have implemented strategies to build several prediction models for f u,p of different drugs. However, these studies did not focus on the identification of responsible molecular fragments to determine the fraction unbound in plasma. In the current work, we tried to focus on the development of robust classification-based QSAR models and evaluated these models with multiple statistical metrics to identify essential molecular fragments/structural attributes for fractions unbound in plasma. The study unequivocally suggests various N-containing aromatic rings and aliphatic groups have positive influences and sulphur-containing thiadiazole rings have negative influences for the f u,p values. The molecular fragments may help for the assessment of the f u,p values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies.

In Silico Design and In Vitro Assessment of Bicyclic Trifluoromethylated Pyrroles as New Antibacterial and Antifungal Agents.

QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q2=0.79±0.02. The consensus prediction for the external evaluation set afforded high predictive power (q2=0.82±0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated in vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by 1H, 19F, and 13C NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39 mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis.

Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation.

This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R2 = 0.813, RMSE = 0.453, MAE = 0.263 in case of training set, and R2 = 0.729, RMSE = 0.635, MAE = 0.477 in case of test set), indicating their reliability in identifying potent FAK inhibitors. Using this QSAR model and molecular docking, compound 21 (ZINC000004523722) was identified as the most potential compound, with predicted logIC50 value and binding energy of 2.59 and - 9.3kcal/mol, respectively. The results of molecular dynamics simulation and ADMET study also further suggested its potential as a promising drug candidate. However, because our research was merely theoretical, additional in vitro and in vivo studies are required for the verification of these results.

In silico insights into design of novel VEGFR-2 inhibitors: SMILES-based QSAR modelling, and docking studies on substituted benzo-fused heteronuclear derivatives

ABSTRACT Eight QSAR models (M1–M8) were developed from a dataset of 118 benzo-fused heteronuclear derivatives targeting VEGFR-2 by Monte Carlo optimization method of CORALSEA 2023 software. Models were generated with hybrid optimal descriptors using both SMILES and Graphs with zero- and first-order Morgan extended connectivity index from a training set of 103 derivatives. All statistical parameters for model validation were within the prescribed limits, establishing the models to be robust and of excellent quality. Among all models, split-2 of M5 was the best-fit as reflected by r v a lidation 2 , Q v a lidation 2 and MAE . Mechanistic interpretation of this model assisted the identification of structural descriptors as promoters and hinderers for VEGFR-2 inhibition. These descriptors were utilized to design novel VEGFR-2 inhibitors (YS01-YS07) by bringing modifications in compound MS90 in the dataset. Docking of all designed compounds, MS90 and sorafenib with VEGFR-2 binding site revealed favourable binding interactions. Docking score of YS07 was higher than that of MS90 and sorafenib. Molecular dynamics simulation study revealed sustained interactions of YS07 with key amino acids of VEGFR-2 at a run time of 100 ns. This study concludes the development of a best fit QSAR model which can assist the design of new anticancer agents targeting VEGFR-2.

Development and validation of PAMPA-BBB QSAR model to determine brain penetration of novel drug candidates

Development and validation of PAMPA-BBB QSAR model to determine brain penetration of novel drug candidates

Development and experimental validation of 3D QSAR models for the screening of thyroid peroxidase inhibitors using integrated methods of computational chemistry

The intricate network of glands and organs that makes up the endocrine system. Hormones are used to regulate and synchronize the nervous and physiological systems. The agents which perturbate an endocrine system are called endocrine disruptors and they can eventually affect cellular proliferation and differentiation in target tissues. A subclass of endocrine disruptors known as thyroid disruptors (TDs) or thyroid disrupting chemicals (TDCs) influence the hypothalamo-pituitary-thyroid axis or directly interfere with thyroid function by binding to thyroid hormone receptors. Thyroid hormone levels in circulation are now included in more test guidelines (OECD TG 441, 407, 408, 414, 421/422, 443/416). Although these might be adequate to recognize thyroid adversity, they are unable to explain the underlying mechanism of action. Thyroid peroxidase (TPO) and sodium iodide symporter (NIS), two proteins essential in the biosynthesis of thyroid hormones, are well-accepted molecular targets for inhibition. The screening of a large number of molecules using high throughput screening (HTS) requires a minimum quantity of sample, cost, and time consuming. Whereas 3-dimensional quantitative structure-activity relationship (3D-QSAR) analysis can screen the TDCs before synthesizing a compound. In the present study, the human TPO (hTPO) and NIS (hNIS) structures were modelled using homology modeling and the quality of the structures was validated satisfactorily using MD simulation for 100ns. Further, 190 human TPO inhibitors with IC50 were curated from Comptox and docked with the modelled structure of TPO using D238, H239 and D240 centric grid. The binding conformation of a molecule with low binding energy was used as a reference and the rest other molecules were aligned after generating the possible conformers. The activity-stratified partition was performed for aligned molecules and training set (139), test set (51) were defined. The machine learning models such as k Nearest Neighbor (kNN) and Random Forest (RF) models were built and validated using external experimental dataset containing 10 molecules. Among the 10 molecules, all 10 molecules were identified as TPO inhibitors and demonstrated 100 % accuracy qualitatively. To confirm the selective TPO inhibition all 10 molecules were docked with the modelled structure of hNIS and the results have demonstrated the selective TPO inhibition.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

Development of 2-dimensional and 3-dimensional QSAR models of Indazole derivatives as TTK inhibitors having Anticancer potential

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r2) of 0.9512, and good internal (q2), and external (pred_r2) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q2) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

Development of 2D and 3D QSAR models of pyrazole derivatives as acetylcholine esterase inhibitors

The drugs that are the most useful in all stages of Alzheimer?s disease (AD) are acetylcholinesterase (AChE) inhibitors. The objectives of this work are to generate various QSAR models for such drugs and to select a robust predictive models from the corresponding models. Studies were then focused on finding a range of pyrazole-like AChE inhibitors by 2D and 3D QSAR analysis. The genetic algorithm-based multiple linear regression (GA-MLR) provided the statistically robust 2D QSAR model that depicted the significance of the molecular volume and the number of multiple bonds along with the presence/ absence of specific atom-centred fragments and topological distance between 2D pharmacophoric features. Furthermore, these results were correlated well with the electrostatic and steric contour maps retrieved from the 3D QSAR (i.e., alignment-dependent molecular field analysis). The 2D QSAR analysis developed a highly statistical and reliable model, which was compared with the mechanistic interpretation of 3D structures and their electrostatic and steric field contributions leading to a predictive 3D QSAR model. The molecule- protein interactions, elicited by molecular docking, corroborated with the field interactions, as revealed by 2D QSAR. Thus, the developed computational models and simulation analyses in the current work provide valuable information for the future design of pyrazole and spiropyrazoline analogs, as potent AChE inhibitors.

In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report.

With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n=37) with soil degradation property (DT50) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal=R2adj: 0.721-0.861, Q2LOO: 0.609-0.757, and external=Q2Fn=0.597-0.933, MAEext=0.174-0.260). Further, the leverage approach of applicability domain assured the model's reliability. The veterinary pharmaceuticals with no experimental values were classified based on their persistence level. Further, the terrestrial toxicity analysis of persistent veterinary pharmaceuticals was done using toxicity prediction by computer assisted technology and in-house built quantitative structure toxicity relationship models to prioritize the toxic and persistent veterinary pharmaceuticals. This study will be helpful in estimation of persistence and toxicity of existing and upcoming veterinary pharmaceuticals.

Quinoline-based Schiff bases as possible antidiabetic agents: ligand-based pharmacophore modeling, 3D QSAR, docking, and molecular dynamics simulations study.

α-Glucosidase enzyme inhibition is a legitimate approach to combat type 2 diabetes mellitus as it manages to control postprandial hyperglycemia. In this pursuit, a literature search identified quinoline-based molecules as potential α-glucosidase inhibitors. Thus our intended approach is to identify pharmacophoric features responsible for the α-glucosidase inhibition. This was achieved by performing, ligand-based pharmacophore modeling, 3D QSAR model development, pharmacophore-based screening of a rationally designed quinoline-based benzohydrazide Schiff base library, identifying, synthesizing and characterizing molecules (6a-6j) by IR, (1H and 13C) NMR, and mass studies. Further, these molecules were evaluated for α-glucosidase and α-amylase inhibitory potential. Compound 6c was found to inhibit α-glucosidase enzyme with an IC50 value of 12.95 ± 2.35 μM. Similarly, compound 6b was found to have an IC50 value of 19.37 ± 0.96 μM as compared to acarbose (IC50: 32.63 ± 1.07 μM); the inhibitory kinetics of compounds 6b and 6c revealed a competitive type of inhibition; the inhibitory effect can be attributed to its mapped pharmacophoric feature and model validation with a survival score of 5.0697 and vector score of 0.9552. The QSAR model showed a strong correlation with an R 2 value of 0.96. All the compounds (6a-6j) showed no toxicity in L929 cell lines by the MTT assay method. Further, the binding orientation and stability of the molecules were assessed using molecular docking studies and MD trajectory analysis. The energy profile of the molecules with protein as a complex and molecules alone was evaluated using MM/GBSA and DFT calculations, respectively; finally, the pharmacokinetic profile was computed using ADMET analysis.

PREDICTION OF ANTIFUNGAL ACTIVITY OF 1,2,4-TRIAZOLE DERIVATIVES USING QSAR MODELS

Purpose. Development of QSAR models and investigation of their effectiveness for predicting the antifungal activity of 1,2,4-triazole derivatives against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes.
 Materials and methods. Experimental data on the antifungal activity of 1,2,4-triazole derivatives against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes were used for scientific research. The data were analyzed using QSAR models based on molecular descriptors automatically generated from structural formulas of 1,2,4-triazole derivatives.
 Results. New QSAR models for predicting antifungal activity against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes based on six physico-chemical parameters of chemicals (eeig11r, eeig09x, r6m+, belm2, eeig12r) are presented. During the comparative analysis of the developed QSAR models, a model was identified that has the best statistical parameters and does not have multicollinearity: MAE=0.136; MAPE=12.55; forecast accuracy=87.45%; MSE=0.028; RMSE=0.167. Among the six factors, the most significant ones were identified.
 Conclusion. As a result of the conducted studies, QSAR models for predicting the antifungal activity of 1,2,4-triazole derivatives against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes were identified and analyzed. From one to six molecular descriptors were taken as factors in the models. The factors that make the greatest contribution to the prediction of antifungal activity were evaluated. The best models are selected based on the calculated statistical parameters.

Fish acute toxicity of nine nanomaterials: Need of pre-tests to ensure comparability and reuse of data

Fish acute toxicity tests are commonly used in aquatic environmental risk assessments, being required in different international substances regulations. A general trend in the toxicity testing of nanomaterials (NMs) has been to use standardized aquatic toxicity tests. However, as these tests were primarily developed for soluble chemical, issues regarding particle dissolution, agglomeration or sedimentation during the time of exposure are not considered when reporting the toxicity of NMs. The aim of this study was to characterize the NM behaviour throughout the fish acute test and to provide criteria to assay the toxicity of nine NMs based on TiO2, ZnO, SiO2, BaSO4, bentonite, and carbon nanotubes, on rainbow trout following OECD Test Guideline (TG) nº203. Our results showed the importance of conducting a preliminary test (without fish) when working with NMs. They provide valuable information on, sample monitoring, agglomeration, sedimentation, dissolution, actual concentrations of NMs, needed to design the test. Among the NMs tested, only bentonite nanoparticles were stable during the 96-h pre-test and test in aquarium water. In contrast, the remaining NMs exhibited considerable loss and sedimentation within the first 24 h. The high sedimentation observed for almost all NMs highlights the need of consistently measuring the concentrations throughout the entire duration of the fish acute toxicity test to make reliable concentration-response relationships. Notable differences emerged in LC50 values when using actual concentrations as nominal concentrations overestimated concentrations by up to 85.6%. Among all NMs tested, only ZnO NMs were toxic to rainbow trout. A flow chart was specifically developed for OECD TG 203, aiding users in making informed decisions regarding the selection of test systems and necessary modifications to ensure accurate, reliable, and reusable toxicity data. Our findings might contribute to the harmonization of TG 203 improving result reproducibility and interpretability and supporting the development of read-across and QSAR models.

Development of QSAR model based on Monte Carlo optimization for predicting GABAA receptor binding of newly emerging benzodiazepines.

Benzodiazepines and their derivatives belong to a category of new psychoactive substances that have been introduced into the continually expanding illicit market. However, there is a notable absence of available pharmacological data for these substances. To gain a deeper understanding of their pharmacology, we employed the Monte Carlo optimization conformation-independent method as a tool for developing QSAR models. These models were built using optimal molecular descriptors derived from both SMILES notation and molecular graph representations. The resulting QSAR model demonstrated robustness and a high degree of predictability, proving to be very reliable. Moreover, we were able to identify specific molecular fragments that exerted both positive and negative effects on binding activity. This discovery paves the way for the swift prediction of binding activity for emerging benzodiazepines, offering a faster and more cost-effective alternative to traditional in vitro/in vivo analyses.

Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates.

Efficiently circumventing the blood-brain barrier (BBB) poses a major hurdle in the development of drugs that target the central nervous system. Although there are several methods to determine BBB permeability of small molecules, the Parallel Artificial Membrane Permeability Assay (PAMPA) is one of the most common assays in drug discovery due to its robust and high-throughput nature. Drug discovery is a long and costly venture, thus, any advances to streamline this process are beneficial. In this study, ∼2,000 compounds from over 60 NCATS projects were screened in the PAMPA-BBB assay to develop a quantitative structure-activity relationship model to predict BBB permeability of small molecules. After analyzing both state-of-the-art and latest machine learning methods, we found that random forest based on RDKit descriptors as additional features provided the best training balanced accuracy (0.70 ± 0.015) and a message-passing variant of graph convolutional neural network that uses RDKit descriptors provided the highest balanced accuracy (0.72) on a prospective validation set. Finally, we correlated in vitro PAMPA-BBB data with in vivo brain permeation data in rodents to observe a categorical correlation of 77%, suggesting that models developed using data from PAMPA-BBB can forecast in vivo brain permeability. Given that majority of prior research has relied on in vitro or in vivo data for assessing BBB permeability, our model, developed using the largest PAMPA-BBB dataset to date, offers an orthogonal means to estimate BBB permeability of small molecules. We deposited a subset of our data into PubChem bioassay database (AID: 1845228) and deployed the best performing model on the NCATS Open Data ADME portal (https://opendata.ncats.nih.gov/adme/). These initiatives were undertaken with the aim of providing valuable resources for the drug discovery community.

QSAR modeling, molecular docking, dynamic simulation and ADMET study of novel tetrahydronaphthalene derivatives as potent antitubercular agents

BackgroundTuberculosis is an air-borne contagious disease caused by slow-growing Mycobacterium tuberculosis (Mtb). According to Global Tuberculosis Report 2022, 1.6 million people were infected by tuberculosis in 2021. The continuing spread of drug-resistant tuberculosis (TB) is one of the most difficult challenges to control the tuberculosis. So new drug discovery is essential to the treatment of tuberculosis. This study aims to develop a QSAR model to predict the antitubercular activity of tetrahydronaphthalene derivatives. The QSARINS was used in this study to develop the QSAR predictive model.ResultsA number of tetrahydronaphthalene derivatives with MIC90 values were obtained from the literature to develop the QSAR predictive model. The compounds were divided into two sets: a training set consisting of 39 compounds and a test set containing 13 compounds. The best predictive Model 4 has R2: 0.8303, Q2LOO: 0.7642, LOF: 0.0550, Q2-F1: 0.7190, Q2-F2: 0.7067, Q2-F3: 0.7938 and CCCext: 0.8720. Based on the developed QSAR equation, the new compounds were designed and subjected to molecular docking, molecular dynamics and ADMET analysis.ConclusionIn the QSAR model, the molecular descriptors of MATS8s, Chi4, bcutv8, Petitjeant and fr_aniline were highly influenced the antitubercular activity. The developed QSAR model helps to predict the antitubercular activity of tetrahydronaphthalene derivatives.

Quantitative Structure–Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity

A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC50 = 0.09 ÷ 160,000 μmol/L) using the GUSAR 2019 software. On the basis of the MNA and QNA descriptors and whole-molecule descriptors using the self-consistent regression, 12 statistically significant consensus models for predicting numerical pIC50 values were constructed. These models demonstrated high predictive accuracy for the training and test sets. Molecular fragments of HSV-1 and HSV-2 TK inhibitors that enhance or diminish the anti-herpetic activity are considered. Virtual screening of the ChEMBL database using the developed QSAR models revealed 42 new effective HSV-1 and HSV-2 TK inhibitors. These compounds are promising for further research. The obtained data open up new opportunities for developing novel effective inhibitors of TK.

Development and application of in silico models to design new antibacterial 5-amino-4-cyano-1,3-oxazoles against colistin-resistant E. coli strains

Here we describe the results of QSAR analysis based on artificial neural networks, synthesis, activity evaluation and molecular docking of a number of 1,3-oxazole derivatives as anti-E. coli antibacterials. All developed QSAR models showed excellent statistics on training (with determination coefficient q2 as 0.76 ± 0.01) and test samples (with q2 as 0.78 ± 0.01). The models were successfully used to identify nine novel 5-amino-4-cyano-1,3-oxazoles with potential anti-E. coli activity. All nine 1,3-oxazoles with predicted high antibacterial potential showed different levels of anti- E. coli in vitro activity. 5-amino-4-cyano-1,3-oxazoles 1 and 3 showed the highest antibacterial activity on average from 17 to 27 mm against MDR, hemolytic MDR and ATCC 25922 E. coli colistin-resistant strains, respectively. The comparative docking analysis demonstrated a possible mechanism of the antibacterial action of the studied 1, 3-oxazoles 1 and 3 through inhibition of E. coli enoyl-ACP reductase (ENR) involved in the biosynthesis of bacterial fatty acids. The localization type is shown of 5-amino-4-cyano-1,3-oxazoles 1 and 3 into the E. coli ENR active site with estimated binding energy from − 10.1 to − 9.5 kcal/mol and hydrogen bonds formation with key amino acids similar to Triclosan. These facts confirm the validity of the hypothesis put forward about the potential antibacterial mechanism of 5-amino-4- cyano-1,3-oxazoles.

Analysis of good and bad fingerprint for identification of NIR based optical frameworks using Monte Carlo method

NIR-based optical materials are widely used in bio-imaging, photodynamic treatment, optoelectronics, sensing and anti-counterfeiting applications. Their advancement has significantly increased the potential for non-invasive imaging, targeted therapy and optical communication systems. Medicine, biology, material science, and telecommunications have all benefited from this progress. Considering this, we herein developed QSAR models using absorption maxima of 292 organic compounds to identify molecular features that can be utilized to shift their λmax towards NIR region. Four splits were developed using Monte Carlo Optimization and index of ideality of correlation (TF2) with CORAL software. Predictability of four models created by these splits was carried out by numerous validation metrics. Model built from second split was observed to be the best with R2validation = 0.8252 and IIC = 0.8423. Mechanistic interpretation was also performed by extracting structural attributes that are responsible for increase and decrease of λmax using correlation weights. Positive structural attributes have been utilized for designing new organic chromophores with red shifted λmax ranging between 791 and 936 nm. The present study offers new horizons for the development of NIR based chromophores.

Computational assessment of the reactivity and pharmaceutical potential of novel triazole derivatives: An approach combining DFT calculations, molecular dynamics simulations, and molecular docking

The worldwide prevalence of cancer and its increasing frequency make it a key research area in drug discovery programs. The current research paper describes the development of QSAR models based on the in vitro against topoisomerase II, which identified the structural origin of anticancer activity for derivatives of triazole moieties linked to mansonone E. The models PLS regression QSARs validated by LOO showed an R2 of 0.92, 0.89 and 0.99 and a Q2 of 0.75, 0.62 and 0.88 for CoMFA, CoMSIA and HQSAR respectively. External validation criteria were used to validate the reliability of the models. These results show the impact of electrostatic and steric fields and of the hydrogen bond donor on the activity of the compounds studied. Based on these results, seven novel inhibitors with high activity were designed, which successfully passed Lipinski's rule of five for oral bioavailability. The evaluations of ADME/Tox parameters and synthetic accessibility for chemical synthesis showed acceptable results. Ligand interactions in binding site protein were assessed using molecular docking. The results show the correct conformational pose of the designed compounds especially the compound T1 where it forms hydrogen and hydrophobic interactions with the main binding site residues. The stability of the complexes was confirmed by the MD study and the calculation of the free binding energy. The T1 synthesis reaction was carried out according to the 1,3 cycloaddition reaction. The study of the local and global reactivity and the energy of activation of this reaction have shown the predicted of the regioselectivity of compound T1. Also we have described the state of transition of two isomers T1,4 and T1.5. Finally, this study would be interesting to help identify and optimize avenues for early discovery of anticancer drugs.