Current Computer - Aided Drug Design - Volume 16, Issue 3, 2020

Background: The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints. Methods: Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model. Results: The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated. Conclusion: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Aim and Objective: Sulfonamides (sulfa drugs) are compounds with a wide range of biological activities and they are the basis of several groups of drugs. Quantitative Structure-Property Relationship (QSPR) models are derived to predict the logarithm of water/ 1-octanol partition coefficients (logP) of sulfa drugs. Materials and Methods: A data set of 43 sulfa drugs was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GAANN) were employed to design the QSPR models. The possible molecular geometries of sulfa drugs were optimized at B3LYP/6-31G* level with Gaussian 98 software. The molecular descriptors derived from the Dragon software were used to build a predictive model for prediction logP of mentioned compounds. The Genetic Algorithm (GA) method was applied to select the most relevant molecular descriptors. Results: The R2 and MSE values of the MLR model were calculated to be 0.312 and 5.074 respectively. R2 coefficients were 0.9869, 0.9944 and 0.9601for the training, test and validation sets of the ANN model, respectively. Conclusion: Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method.

Background: B-RafV600E kinase was identified as an important target in current cancer treatment, and the type II B inhibitors show good qualities in preclinical studies. Therefore, it is very important to discover novel II B inhibitors of B-RafV600E kinase. Methods: In order to discover novel II B inhibitors of B-RafV600E kinase, virtual screening against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3DQSAR model and binding free energy (ΔGbind) calculation studies. The inhibitory activities against A375 cell lines of the hit compounds were tested by using MTT assay. Results: Five promising hit compounds were obtained after screening, and all the five hit compounds showed good inhibitory rates against A375 cell lines. Conclusion: The combined approach of the virtual screening in our work is effective, which can be used to discover novel inhibitors with a new skeleton. In addition, the five compounds obtained from the screening showed good inhibitory rates against A375 cell lines, which can be considered to develop new II B inhibitors of B-RafV600E kinase.

Background: Plant-based drugs provide an outstanding contribution to modern therapeutics, and it is well known that the presence of different phytochemicals is responsible for such pharmacological effects. Carthamus tinctorius L. is one such medicinally important plant whose different solvent extracts have been reported with several pharmacological effects like antibacterial, hepatoprotective, and wound healing. The exploration of phytoconstituents from such a medicinally important plant for different pharmacological effects could produce new and effective drugs to treat human diseases. Objective: The present study attempts to explore the antibacterial and anthelmintic properties of dehydroabietylamine, a diterpene isolated from Carthamus tinctorius L. followed by the in silico elucidation of its probable mode of action. Methods: The minimum inhibition concentration (MIC) and minimum bactericidal concentration (MBC) of dehydroabietylamine were assessed against Staphylococcus aureus and Pseudomonas aeruginosa, using micro- broth dilution method. The anthelmintic activity of was determined to assess the time taken for paralysis and death of Pheretima Posthuma at different concentrations. Additionally, molecular docking study was conducted to understand the interaction between dehydroabietylamine with target proteins identified for both antibacterial and anthelmintic activity viz., glucosamine-6-phosphate synthase and β-Tubulin, respectively. Results: The dehydroabietylamine showed the significant MIC for S. aureus (12.5 μg/ml) and P. aeruginosa (6.25μg/ml), respectively. The result of anthelmintics effect of dehydroabietylamine was found to be dosedependent and compared to the standard drug, albendazole. Conclusion: The interactions of dehydroabietylamine with the two target proteins with high binding affinity indicated the probable inhibition of target proteins, which could be the cause for prominent antibacterial and antihelminthic effects.

Background: Chemotherapy and radiotherapy have negative effects on normal tissues and they are very expensive and lengthy treatments. These disadvantages have recently attracted researchers to the new methods that specifically affect cancerous tissues and have lower damage to normal tissues. One of these methods is the use of intelligent recombinant fusion toxin. The fusion toxin DTGCSF, which consists of linked Diphtheria Toxin (DT) and Granulocyte Colony Stimulate Factor (GCSF), was first studied by Chadwick et al. in 1993 where HATPL linker provided the linking sequence between GCSF and the 486 amino acid sequences of DT. Methods: In this study, the fusion toxin DT389GCSF is evaluated for functional structure in silico. With the idea of the commercial fusion toxin of Ontak, the DT in this fusion protein is designed incomplete for 389 amino acids and is linked to the beginning of the GCSF cytokine via the SG4SM linker (DT389GCSF). The affinity of the DT389GCSF as a ligand with GCSF-R as receptor was compared with DT486GCSF as a ligand with GCSF-R as receptor. Both DT486GCSF and its receptor GCSF-R have been modeled by Easy Modeler2 software. Our fusion protein (DT389GCSF) and GCSF-R are modeled through Modeller software; all of the structures were confirmed by server MDWEB and VMD software. Then, the interaction studies between two proteins are done using protein-protein docking (HADDOCK 2.2 web server) for both the fusion protein in this study and DT486GCSF. Results: The HADDOCK results demonstrate that the interaction of DT389GCSF with GCSF-R is very different and has a more powerful interaction than DT486GCSF with GCSF-R. Conclusion: HADDOCK web server is operative tools for evaluation of protein–protein interactions, therefore, in silico study of DT389GCSF will help with studying the function and the structure of these molecules. Moreover, DT389GCSF may have important new therapeutic applications.

Background: Prostate cancer is one of the most common tumors in the world and the fifth leading cause of male cancer death. Although the treatment of localized androgen-dependent prostate cancer has been successful, the efficacy of androgen-independent metastatic disease is limited. Curcumin, a natural product, has been found to inhibit the proliferation of prostate cancer cells. Objective: To design curcumin analogs with higher biological activity and lower toxicity and side effects for the treatment of prostate cancer. Methods: In this study, the three dimensional-quantitative structure activity relationship (3DQSAR) and molecular docking studies were performed on 34 curcumin analogs as anti-prostate cancer compounds. We introduced OSIRIS Property Explorer to predict drug-related properties of newly designed compounds. Results: The optimum CoMSIA model exhibited statistically significant results: the cross-validated correlation coefficient q2 is 0.540 and non-cross-validated R2 value is 0.984. The external predictive correlation coefficient Rext 2 is 0.792. The information of structure-activity relationship can be obtained from the CoMSIA contour maps. In addition, the molecular docking study of the compounds for 3ZK6 as the protein target revealed important interactions between active compounds and amino acids. Conclusion: Compound 28i may be a new type of anti-prostate cancer drug with higher biological activity and more promising development.

Background: The theory of partial order is a branch of Discrete Mathematics and is often seen as pretty esoteric. However, depending on a suitable definition of an order relation, partial order theory has some statistical flavor. Here we introduce the application of partial order for environmental chemistry. Objective: We showed that partial order is an instrument, which at the same time, has both data exploration - and evaluation potency. Methods: The partial order theory was applied in this study. It depends on four indicators which describe the environmental hazards of chemicals. Results: Nineteen organic chemicals were found within a monitoring study in the German river Main and were taken as an exemplary case. The results indicated that chemicals can have a high risk on the environment, however, the type of risk is different and should not conceptually merge into a single quantity. Conclusion: Partial order theory is of help to define different regulations and environmental management plans.

Background: The discovery of clinically relevant EGFR inhibitors for cancer therapy has proven to be a challenging task. To identify novel and potent EGFR inhibitors, the quantitative structure-activity relationship (QSAR) and molecular docking approach became a very useful and largely widespread technique for drug design. Methods: We performed the in vitro cytotoxic activity on HEPG-2 cell line and earlier on MCF-7 and A 549 by using MTT assay method. The development of 3D QSAR model of N1,N4-bis(2-oxoindolin-3- ylidene) succinohydrazides using the stepwise-backward variable methods to generate Multiple Linear Regression method elucidates the structural properties required for EGFR inhibitory activity and also perform the Molecular Docking studies on EGFR (PDB ID:1M17). Further, we analysed for Lipinski’s rule of five to evaluate the drug-likeness and established in silico ADMET properties. Results: The resulting cytotoxicity (IC50) values ranged from 9.34 to 100 μM and compared with cisplatin as a standard. Among the series of compounds, 6j showed good cytotoxic activity on HEPG-2 cell line with 9.34 μM, IC50 value. Most of the evaluated compounds showed good antitumor activity on HEPG-2 than MCF-7and A549. The developed 3D QSAR Multiple Linear Regression models are statistically significant with non-cross-validated correlation coefficient r2 = 0.9977, cross-validated correlation coefficient q2 = 0.902 and predicted_r2 = 0.9205. Molecular docking studies on EGFR (PDB ID: 1M17) results, compounds 6d, 6j and 6l showed good dock/PLP scores i.e. -81.28, -73.98 and -75.37, respectively, by interacting with Leu-694, Val-702 and Gly-772 amino acids via hydrophobic and hydrogen bonds with Asn818 and Met- 769. Further, we analysed drug-likeness and established in silico ADMET properties. Conclusion: The results of 3D QSAR studies suggest that the electrostatic and steric descriptors influence the cytotoxic activity of succinohydrazides. From the molecular docking studies, it is evident that hydrophobic, hydrogen and Van Der Waal’s interactions determine binding affinities. In addition to this, druglikeness and ADMET properties were analysed. It is evident that there is a correlation between the QSAR and docking results. Compound 6j was found to be too lipophilic due to its dihalo substitution on isatin nucleus, and can act as a lead molecule for further and useful future development of new EGFR Inhibitors.

Background: Piperine or piperic acid was isolated from fruits of Piper nigrum and had been reported as pharmacological valuable bioactive constituents. Keeping in view, a series of piperic acid-based N heterocyclic’s derivatives were synthesized and evaluated for antibacterial activity. All these prepared ligands were docked to study the molecular interactions and binding affinities against the protein PDB ID: 5 CDP. Objective: To meet the real need of newer antibacterials, we designed and synthesized scaffolds with good antibacterial activity. The obtained antibacterials have been validated in terms of ligand-protein interaction and thus prove to build up as good drug candidates. Methods: Antibacterial activity of the compounds were carried out against bacterial strains; three Grampositive and three Gram-negative bacterial strains using agar well diffusion method. In silico molecular docking studies were carried out using Glide (grid-based ligand docking) program incorporated in the Schrödinger molecular modeling package by Maestro 11.0. Results: Compounds BC 28, BC 32, and BC 33 exhibits antibacterial activity along with Glide docking score of -8.580, -9.753 kcal/mol, and -8.813 kcal/mol, respectively. Docking studies explained hydrogen bonding, pi-pi, and hydrophobic interactions with amino acid residues which explain the binding affinity of the most docked ligand with protein. Conclusion: In the present study, substituted piperic acid was synthesized and evaluated as antibacterial compared with standard drug ciprofloxacin and results interpret that having nitrogen as heteroatom in the heterocyclic nucleus found to be more potent than the standard drug ciprofloxacin. On comparing, substitution with electron-donating groups generates excellent antibacterial potential against the bacterial strains. It was also proved that having substitution with electron-donating groups on meta and para position with triazoline ring system exhibits greater potential while compounds which have a meta- electron-donating substituent showed lesser activity with thiazole nucleus. In addition, structure-based activities of the prepared analogs were discussed under Structure-Activity Relationship (SAR) section.

Background: Lately, diabetes has become the main health concern for millions of people around the world. Dipeptidyl peptidase-IV (DPP-IV) inhibitors have emerged as a new class of oral antidiabetic agents. Formerly, acridines, N4-sulfonamido-succinamic, phthalamic, acrylic and benzoyl acetic acid derivatives, and sulfamoyl-phenyl acid esters were designed and developed as new DPP-IV inhibitors. Objective: This study aims to develop a pharmacophore model of DPP-IV inhibitors and to evaluate phenanthridines as a novel scaffold for inhibiting DPP-IV enzyme. In addition, to assess their binding interactions with the enzyme through docking in the binding site of 4A5S (PDB). Methods: Herein, Quantum–Polarized Ligand Docking (QPLD) and ligand-based pharmacophore modeling investigations were performed. Three novel 3,8-disubstituted-6-phenyl phenanthridine derivatives 3-5 have been designed, synthesized and characterized. In vitro biological testing against DPP-IV was carried out using fluorometric assay kit. Results: QPLD study demonstrates that compounds 3-5 forms H-bond with Lys554, Trp629, and Tyr631, besides charge transfer interaction between their aromatic rings and the aromatic rings of Tyr547 and Tyr666. Moreover, they fit the three pharmacophoric point features of DPP-IV inhibitors and were proven to have in vitro DPP-IV inhibitory activity where compound 5 displayed a % inhibition of 45.4 at 100 μM concentration. Conclusion: Phenanthridines may serve as a potential lead compound for developing new DPP-IV inhibitors as a promising antidiabetic agent. Computational results suggest future structural simplification.

Background: Urolithiasis is the process of forming stones in the kidney, bladder, and/or urinary tract. It has been reported that kidney stones are the third most common disorder among urinary diseases. At present, surgical procedures and Extracorporeal Shock Wave Lithotripsy (ESWL) are commonly employed for the treatment of Urolithiasis. The major drawback of these procedures is the recurrence of stones. Methods: This study aimed to identify potential natural inhibitors against human Serum Albumin (SA) from the plant Scoparia Dulsis for Urolithiasis. As protein-ligand interactions play a key role in structure- based drug design, this study screened 26 compounds from Scoparia Dulsis and investigated their binding affinity against SA by using molecular docking. The three dimensional (3D) structure of SA was retrieved from Protein Data Bank (PDB) and docked with PubChem structures of 26 compounds using PyRX docking tool through Autodock Vina. Moreover, a 3D similarity search on the PubChem database was performed to find the analogs of best scored compound and docking studies were performed. Drug-likeness studies were made using Swiss ADME and Lipinski’s rule of five was performed for the compounds to evaluate their anti-urolithiatic activity. Results: The results showed that citrusin c (Eugenyl beta-D-glucopyranoside) exhibited best binding energy of -8.1 kcal/mol with SA followed by aphidicolin, apigenin, luteolin and scutellarein. Two compounds (PubChem CID 46186820, PubChem CID 21579141) analogous to citrusin c were selected based on the lowest binding energy. Conclusion: This study, therefore, reveals that these compounds could be promising candidates for further evaluation for Urolithiasis prevention or management.

Background: Insulin-dependent Diabetes Mellitus Type 1 (T1D) also referred to as autoimmune diabetes. T1D is a chronic disease which is characterized by way of insulin deficiency. The deficiency is due to the loss of pancreatic β cells and leads to hyperglycemia. There are many factors which play a significant role in T1D disease pathogenicity including genetic predisposition, the immune system, and environmental factors. The environmental factors may include Coxsackie B4 virus, a small RNA virus. Objective: The objective of current in silico study is to identify active lead compounds against Coxsackie B4 virus, a small RNA virus which has been reported in diabetic patients after PCR. There is a need to predict inhibitors against TID caused by Coxsackie B4 viral protein that may be used as a drug against TID in the future. Methods: For this purpose, different bioinformatics databases and tools were used. The protein structure generation and validation, retrieval of ligands and their properties analysis were performed by different databases, web servers, and software tools. Moreover, the docking tools were used to identify the target site of the protein and interaction of different inhibitors with the target protein molecule. Results: Based on the analysis, two lead compounds ZINC00034488 and ZINC00034585 were selected as potential drugs. These compounds are non-toxic and have best interaction energy and fulfill Lipinski rule, Veber rule, Ghose Rule, Weighted QED, Unweighted QED and BBB likeness parameters. Conclusion: Our work will help researchers to get an idea about the understanding of chemicals against Coxsackie B4 Viruses and helpful to design a drug and test these chemicals to overcome Diabetes Mellitus Type 1 caused by Coxsackie B4 virus.

Background: The relatedness between the linear equations of thermodynamics and QSAR was studied thanks to the recently elucidated crystal structure complexes between sulfonamide pterin conjugates and dihydropteroate synthase (DHPS) together with a published set of thirty- six synthetic dapsone derivatives with their reported entropy-driven activity data. Only a few congeners were slightly better than dapsone. Objective: Our study aimed at demonstrating the applicability of thermodynamic QSAR and to shed light on the mechanistic aspects of sulfone binding to DHPS. Methods: To this end ligand docking to DHPS, quantum mechanical properties, 2D- and 3D-QSAR as well as Principle Component Analysis (PCA) were carried out. Results: The short aryl substituents of the docked pterin-sulfa conjugates were outward oriented into the solvent space without interacting with target residues which explains why binding enthalpy (ΔH) did not correlate with potency. PCA revealed how chemically informative descriptors are evenly loaded on the first three PCs (interpreted as ΔG, ΔH and ΔS), while chemically cryptic ones reflected higher dimensional (complex) loadings. Conclusion: It is safe to utter that synthesis efforts to introduce short side chains for aryl derivatization of the dapsone scaffold have failed in the past. On theoretical grounds we provide computed evidence why dapsone is not a pharmacodynamic lead for drug profiling because enthalpic terms do not change significantly at the moment of ligand binding to target.

Introduction: Rheumatoid Arthritis [RA] is an autoimmune disease that can cause chronic inflammation of the joints. Human DiHydroOrotate DeHydrogenase [DHODH] is a clinically validated drug target for the treatment of Rheumatoid Arthritis. DHODH inhibition results in beneficial immunosuppressant and anti-proliferative effects. Materials and Methods: Leflunomide [LEF] and Brequinar Sodium [BREQ], drugs used in the treatment of RA, suppresses the immune cells responsible for inflammation but has several side-effects, most predominant being symptomatic liver damage and toxicity. An existing scaffold based on structural analogies with LEF and BREQ was used to screen out potent inhibitors of DHODH, in ZINC Database using 2D binary fingerprint. 10 structures similar to the scaffold were shortlisted due to their Tanimoto similarity coefficient. Selected structures were docked using the tools AutoDock, Ligand fit and iGEMDOCK with target human DHODH. High scoring compounds having similar interactions as that of scaffold were checked to evaluate their Drug-Likeliness. Results: The five shortlisted compounds were then subjected to Molecular Dynamics Simulation studies for 50ns using GROMACS. Measures of structural similarity based on 2D Fingerprint Screening and Molecular Dynamics Simulation studies can suggest good leads for drug designing. The novelty of this study is that the workflow used here yields the same results that are at par with the experimental data. Conclusion: This suggests the use of the 2D fingerprint similarity search in various databases, followed by multiple docking algorithms and dynamics as a workflow that will lead to finding novel compounds that a structurally and functionally similar to LEF and BREQ.