Virtual Screening Methods Research Articles

Virtual Screening to Identify the Protein Network Interaction of Theophylline in Red Complex Pathogens

Background: Plants, herbs and plant derived compounds have been used since decades in folk and traditional medicine. These compounds were found to be non-toxic and compatible with the human cells. Screening of this exhaustive collection of compounds seems to be a herculean task. Hence, virtual screening methods have been developed to predict the potential targets of these compounds enabling researchers to acquire preliminary data on the compound intended to be tested in vitro.
 Aim: The present study aims to identify protein targets of theophylline in red complex pathogens which are mainly associated with periodontitis.
 Methods: The STITCH v5.0 pipeline was mainly used to classify drug-protein interactions, while VirulentPred and VICMPred were used to determine the proteins' virulence properties and functional classes. PSORTb v3.0 was used to determine the subcellular localization of viral proteins, and BepiPred v1.0 Linear Epitope Prediction was used to classify epitopes.
 Results: Proteins of red complex pathogens implicated in cellular process, metabolism, and virulence were identified to interact with theophylline. The virulent proteins attacked by the drugs were present in the cytoplasm, which would improve the drugs' antimicrobial efficacy even more. Finally, epitope prediction showed a number of epitopes in virulent proteins that can be targeted.
 Conclusions: Proteins involved in the cellular process, metabolism, and virulence of red complex pathogens targeted by theophylline were identified. To substantiate the true interactions between the drugs and the protein arsenal of pathogens, further in vitro experiments on a broad range of pathogens are required.

Virtual screening and in vitro validation of natural compound inhibitors against SARS-CoV-2 spike protein

The COVID-19 pandemic caused by the SARS-CoV-2 virus has led to a major public health burden and has resulted in millions of deaths worldwide. As effective treatments are limited, there is a significant requirement for high-throughput, low resource methods for the discovery of novel antivirals. The SARS-CoV-2 spike protein plays a key role in viral entry and has been identified as a therapeutic target. Using the available spike crystal structure, we performed a virtual screen with a library of 527 209 natural compounds against the receptor binding domain of this protein. Top hits from this screen were subjected to a second, more comprehensive molecular docking experiment and filtered for favourable ADMET properties. The in vitro activity of 10 highly ranked compounds was assessed using a virus neutralisation assay designed to facilitate viral entry in a physiologically relevant manner via the plasma membrane route. Subsequently, four compounds ZINC02111387, ZINC02122196, SN00074072 and ZINC04090608 were identified to possess antiviral activity in the µM range. These findings validate the virtual screening method as a tool for identifying novel antivirals and provide a basis for future drug development against SARS-CoV-2.

Rational design of inhibitors against LpxA protein of Acinetobacter baumannii using a virtual screening method

BackgroundAcinetobacter baumannii is a highly antimicrobial resistant nosocomial pathogen. Resistance to currently used antibiotics has limited effective drugs against this bacterium. This study aimed to propose a rational inhibitor design against the LpxA protein of A. baumannii using a virtual screening method based on a similar structure of ligands. MethodsIn this study, we targeted LpxA protein, which is involved in the early stage of LPS biosynthesis. In the next step, we used Peptide920 and 1,2- Ethanediol as templates to find similar compounds using Drugbank and Zinc15 webservers, respectively. Subsequently, molecular dynamics (MD) simulations were carried out for LpxA protein and two complexes of ZINC895081 and Macrolactam-1 which represented the highest binding affinity and best conformation. Finally, ADMET properties, water solubility and drug-likeness of the desired compounds were evaluated using SwissADME and DruLiTo softwares. ResultsAccording to considered criteria, Drugbank suggested 5 compunds including Ilomastat, Macrolactam-1, Macrolactam-2, Macimorelin, and Oglufanide. On the other hand, Zinc15 webserver suggested 4 compunds including ZINC895048, ZINC895081, ZINC901061 and ZINC1531008. The result of the HDOCK server and Molegro virtual docker (MVD) showed that Macrolactam-1 and ZINC895081 (Citrate) had the highest docking score. In addition, MD simulations showed that ZINC895081 and Macrolactam-1 ligands have the stable binding to the LpxA protein. According to Lipinski's rule, these two compounds are non-carcinogenic, non-toxic and promising inhibitors against LpxA of A. baumannii. ConclusionIt seems that Macrolactam-1 and ZINC895081 (Citrate) are two valuable promising inhibitors against the LpxA protein of A. baumannii. Further in vitro and in vivo experiments are needed to confirm the capabilities of these proposed compounds against A. baumannii.

Discovery of microtubule stabilizers with novel scaffold structures based on virtual screening, biological evaluation, and molecular dynamics simulation

Disrupting the dynamics and structures of microtubules can perturb mitotic spindle formation, cause cell cycle arrest in G2/M phase, and subsequently lead to cellular death via apoptosis. In this investigation, the structure-based virtual screening methods, including molecular docking and rescoring, and similarity analysis of interaction molecular fingerprints, were developed to discover novel tubulin inhibitors from ChemDiv database with 1,601,806 compounds. The screened compounds were further filtered by PAINS, ADME/T, Toxscore, SAscore, and Drug-likeness analysis. Finally, 17 hit compounds were selected, and then submitted to the biologic evaluation. Among these hits, the P2 exhibited the strongest antiproliferative activity against four tumor cells including HeLa, HepG2, MCF-7, and A549. The in vitro tubulin polymerization assay revealed P2 could promote tubulin polymerization in a dose dependent manner. Finally, in order to analyze the interaction modes of complexes, the molecular dynamics simulation was performed to investigate the interactions between P2 and tubulin. The molecular dynamics simulation analysis showed that P2 could stably bind to taxane site, induced H6–H7, B9–B10, and M-loop regions changes. The ΔGbind energies of tubulin-P2 and tubulin-paclitaxel were −68.25 ± 12.98 and −146.05 ± 16.17 kJ mol−1, respectively, which were in line with the results of the experimental test. Therefore, P2 has been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.

MultiPharm-DT: A Multi-Objective Decision Tool for Ligand-Based Virtual Screening Problems

Ligand Based Virtual Screening methods are used to screen molecule databases to select the most promising compounds for a query. This is performed by decision-makers based on the information of the descriptors, which are usually processed individually. This methodology leads to a lack of information and hard post-processing dependent on the expert’s knowledge that can end up in the discarding of promising compounds. Consequently, in this work, we propose a new multi-objective methodology called MultiPharm-DT where several descriptors are considered simultaneously and whose results are offered to the decision-maker without effort on their part and without relying on their expertise.

Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria.

Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.

Identification of novel MurA inhibitors using in silico approach, their validation and elucidation of mode of inhibition

UDP-N-acetylglucosamine enolpyruvyl transferase (MurA) is an important enzyme involved in the first cytosolic step of bacterial cell wall synthesis. In this study a combination of ligand based and structure based in silico virtual screening methods were utilised for screening of more than 50,000 drug-like compounds from CSIR-IIIM in-house compound library in order to identify potent inhibitors of MurA. The identified hits were validated in vitro under various incubation conditions using Malachite green phosphate assay, and two potent hits viz 3772-9534 and D396-0012 were identified. Among these hits, compound 3772-9534 showed significant changes in the activity values in different assay conditions. The MD simulation study of 3772-9534 suggested a novel binding site in MurA enzyme, independent of the two-substrate binding sites. Binding of inhibitors at the allosteric site induces conformational changes in the enzyme, which leads to inhibition of enzymatic activity. Overall, the study offers new insight for targeting MurA, which may promote the discovery of novel MurA allosteric site inhibitors.

Using Stochastic Computing for Virtual Screening Acceleration

Stochastic computing is an emerging scientific field pushed by the need for developing high-performance artificial intelligence systems in hardware to quickly solve complex data processing problems. This is the case of virtual screening, a computational task aimed at searching across huge molecular databases for new drug leads. In this work, we show a classification framework in which molecules are described by an energy-based vector. This vector is then processed by an ultra-fast artificial neural network implemented through FPGA by using stochastic computing techniques. Compared to other previously published virtual screening methods, this proposal provides similar or higher accuracy, while it improves processing speed by about two or three orders of magnitude.

Machine Learning-Based Virtual Screening Strategy RevealsSome Natural Compounds as Potential PAK4 Inhibitors in Triple Negative Breast Cancer

Background: P-21 activating kinase 4 (PAK4) is implicated in the poor prognosis of many cancers, especially in the progression of Triple Negative Breast Cancer (TNBC). The present study was aimed at designing some potential drug candidates as PAK4 inhibitors for breast cancer therapy. Objective: This study aimed to finding novel inhibitors of PAK4 from natural compounds using computational approach. Methods: An e-pharmacophore model was developed from docked PAK4-co-ligand complex and used to screen over a thousand natural compounds downloaded from BIOFACQUIM and NPASS databases to match a minimum of 5 sites for selected (ADDDHRR) hypothesis. The robustness of the virtual screening method was accessed by well-established methods including EF, ROC, BEDROC, AUAC, and the RIE. Compounds with fitness score greater than one were filtered by applying molecular docking (HTVS, SP, XP and Induced fit docking) and ADME prediction. Using Machine learning-based approach QSAR model was generated using Automated QSAR. The computed top model kpls_des_17 (R2= 0.8028, RMSE = 0.4884 and Q2 = 0.7661) was used to predict the pIC50 of the lead compounds. Internal and external validations were accessed to determine the predictive quality of the model. Finally, the binding free energy calculation was computed. Results: The robustness/predictive quality of the models was affirmed. The hits had better binding affinity than the reference drug and interacted with key amino acids for PAK4 inhibition. Overall, the present analysis yielded three potential inhibitors that are predicted to bind with PAK4 better than the reference drug tamoxifen. The three potent novel inhibitors, vitexin, emodin and ziganein recorded IFD score of -621.97 kcal/mol, -616.31 kcal/mol and -614.95 kcal/mol, respectively while showing moderation for ADME properties and inhibition constant. Conclusion: It is expected that the findings reported in this study may provide insight for designing effective and less toxic PAK4 inhibitors for triple negative breast cancer.

Novel Elongator Protein 2 Inhibitors Mitigating Tumor Necrosis Factor-α Induced Osteogenic Differentiation Inhibition.

Tumor necrosis factor-α is a common cytokine that increases in inflammatory processes, slows the differentiation of bone formation, and induces osteodystrophy in the long-term inflammatory microenvironment. Our previous study confirmed that the Elongation protein 2 (ELP2) plays a significant role in osteogenesis and osteogenic differentiation, which is considered a drug discovery target in diseases related to bone formation and differentiation. In this study, we applied an in silico virtual screening method to select molecules that bind to the ELP2 protein from a chemical drug molecule library and obtained 95 candidates. Then, we included 11 candidates by observing the docking patterns and the noncovalent bonds. The binding affinity of the ELP2 protein with the candidate compounds was examined by SPR analysis, and 5 out of 11 compounds performed good binding affinity to the mouse ELP2 protein. After in vitro cell differentiation assay, candidates 2# and 5# were shown to reduce differentiation inhibition after tumor necrosis factor-α stimulation, allowing further optimization and development for potential clinical treatment of inflammation-mediated orthopedic diseases.

Exploration of SARS-CoV-2 3CLpro Inhibitors by Virtual Screening Methods, FRET Detection, and CPE Assay.

COVID-19 caused by a novel coronavirus (SARS-CoV-2) has been spreading all over the world since the end of 2019, and no specific drug has been developed yet. 3C-like protease (3CLpro) acts as an important part of the replication of novel coronavirus and is a promising target for the development of anticoronavirus drugs. In this paper, eight machine learning models were constructed using naïve Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular fingerprints. The optimal models were selected according to the results of 5-fold cross verification, test set verification, and external test set verification. A total of 5766 natural compounds from the internal natural product database were predicted, among which 369 chemical components were predicted to be active compounds by the optimal models and the EstPGood values were more than 0.6, as predicted by the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were selected for further biological activity determination by the fluorescence resonance energy transfer (FRET) method and cytopathic effect (CPE) detection. The results indicated that (+)-shikonin, shikonin, scutellarein, and 5,3′,4′-trihydroxyflavone showed certain activity in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory concentration (IC50) values ranging from 4.38 to 87.76 μM. In the CPE assay, 5,3′,4′-trihydroxyflavone showed a certain antiviral effect with an IC50 value of 8.22 μM. The binding mechanism of 5,3′,4′-trihydroxyflavone with SARS-CoV-2 3CLpro was further revealed through CDOCKER analysis. In this study, 3CLpro prediction models were constructed based on machine learning algorithms for the prediction of active compounds, and the activity of potential inhibitors was determined by the FRET method and CPE assay, which provide important information for further discovery and development of antinovel coronavirus drugs.

Characterization of Soybean Protein Isolate-Food Polyphenol Interaction via Virtual Screening and Experimental Studies.

(1) Background: Protein–polyphenol interactions have been widely studied regarding their influence on the properties of both protein and the ligands. As an important protein material in the food industry, soybean protein isolate (SPI) experiences interesting changes through polyphenols binding. (2) Methods: In this study, a molecular docking and virtual screening method was established to evaluate the SPI–polyphenol interaction. A compound library composed of 33 commonly found food source polyphenols was used in virtual screening. The binding capacity of top-ranking polyphenols (rutin, procyanidin, cyanidin chloride, quercetin) was validated and compared by fluorescence assays. (3) Results: Four out of five top-ranking polyphenols in virtual screening were flavonoids, while phenolic acids exhibit low binding capacity. Hydrogen bonding and hydrophobic interactions were found to be dominant interactions involved in soybean protein–polyphenol binding. Cyanidin chloride exhibited the highest apparent binding constant (Ka), which was followed by quercetin, procyanidin, and rutin. Unlike others, procyanidin addition perturbed a red shift of SPI fluorescence, indicating a slight conformational change of SPI. (4) Conclusions: These results suggest that the pattern of SPI–polyphenol interaction is highly dependent on the detailed structure of polyphenols, which have important implications in uncovering the binding mechanism of SPI–polyphenol interaction.

Molecular Insights into Binding Mode and Interactions of Structure-Based Virtually Screened Inhibitors for Pseudomonas aeruginosa Multiple Virulence Factor Regulator (MvfR).

Multi-drug resistance (MDR) bacterial pathogens pose a threat to global health and warrant the discovery of new therapeutic molecules, particularly those that can neutralize their virulence and stop the evolution of new resistant mechanisms. The superbug nosocomial pathogen, Pseudomonas aeruginosa, uses a multiple virulence factor regulator (MvfR) to regulate the expression of multiple virulence proteins during acute and persistent infections. The present study targeted MvfR with the intention of designing novel anti-virulent compounds, which will function in two ways: first, they will block the virulence and pathogenesis P. aeruginosa by disrupting the quorum-sensing network of the bacteria, and second, they will stop the evolution of new resistant mechanisms. A structure-based virtual screening (SBVS) method was used to screen druglike compounds from the Asinex antibacterial library (~5968 molecules) and the comprehensive marine natural products database (CMNPD) (~32 thousand compounds), against the ligand-binding domain (LBD) of MvfR, to identify molecules that show high binding potential for the relevant pocket. In this way, two compounds were identified: Top-1 (4-((carbamoyloxy)methyl)-10,10-dihydroxy-2,6-diiminiodecahydropyrrolo[1,2-c]purin-9-yl sulfate) and Top-2 (10,10-dihydroxy-2,6-diiminio-4-(((sulfonatocarbamoyl)oxy)methyl)decahydropyrrolo[1,2-c]purin-9-yl sulfate), in contrast to the co-crystallized M64 control. Both of the screened leads were found to show deep pocket binding and interactions with several key residues through a network of hydrophobic and hydrophilic interactions. The docking results were validated by a long run of 200 ns of molecular dynamics simulation and MM-PB/GBSA binding free energies. All of these analyses confirmed the presence of strong complex formation and rigorous intermolecular interactions. An additional analysis of normal mode entropy and a WaterSwap assay were also performed to complement the aforementioned studies. Lastly, the compounds were found to show an acceptable range of pharmacokinetic properties, making both compounds potential candidates for further experimental studies to decipher their real biological potency.

Novel natural scaffold as hURAT1 inhibitor identified by 3D-shape-based, docking-based virtual screening approach and biological evaluation

As a promising therapeutic target for gout, hURAT1 has attracted increasing attention. In this work, we identified a novel scaffold of hURAT1 inhibitors from a personal natural product database of verified herb-treated gout. First, we constructed more than 800 natural compounds from Chinese medicine that were verified to treat gout. Following the application of both shape-based and docking-based virtual screening (VS) methods, taking into account the shape similarity and flexibility of the target, we identified isopentenyl dihydroflavones that might inhibit hURAT1. Specifically, 9 compounds with commercial availability were tested with biochemical assays for the inhibition of 14C-uric acid uptake in high-expression hURAT1 cells (HEK293-hURAT1), and their structure–activity relationship was evaluated. As a result, 8-isopentenyl dihydroflavone was identified as a novel scaffold of hURAT1 inhibitors since isobavachin (DHF3) inhibited hURAT1 with an IC50 value of 0.39 ± 0.17 μM, which was comparable to verinurad with an IC50 value of 0.32 ± 0.23 μM. Remarkably, isobavachin also displayed an eminent effect in the decline of serum uric acid in vivo experiments. Taken together, isobavachin is a promising candidate for the treatment of hyperuricemia and gout.

Virtual Screening to Identify the Protein Network Interactions of Triclosan with Streptococcus mutans and Enterococcus faecalis

Introduction: Triclosan is considered to be an important ingredient in toothpastes and mouth rinses. Several studies have reported contradictory results regarding the antimicrobial effect of triclosan. Hence, the present in silico study intends to identify the potential targets of triclosan in two common dental pathogens Streptococcus mutans and Enterococcus faecalis. 
 Aim: To identify the protein network interactions of triclosan in Streptococcus mutans and Enterococcus faecalis by virtual screening method.
 Materials and Methods: The STITCH v5.0 database was initially used for identifying drug-protein interactions followed by VICMPred and VirulentPred which was employed to identify functional class of the proteins and its virulence property. Finally, BepiPred v1.0 Linear Epitope Prediction tool was used to identify the potential epitopes of the virulent proteins.
 Results: Triclosan was found to interact with crucial proteins in S. mutans and E. faecalis which could contribute to severe forms of periodontitis and endodontic diseases.
 Conclusion: Taken together, the present study provides the preliminary data on the potential targets of triclosan in common dental pathogens. Further experimental validation is warranted to provide concrete evidence on the molecular targets of dental pathogens.

Virtual Screening to Identify the Protein Network Interaction of Berberine with Red Complex Pathogens

Introduction: Periodontal disease is an infection of the tissues that hold your teeth in place. It's typically caused by poor brushing and flossing habits that allow plaque, a sticky film of bacteria, to build up on the teeth and harden. Elimination of these pathogens from the site of infection remains a perplexing task, which demands the use of antibiotics. The emergence of drug resistant forms has spurred interest into identifying novel therapeutic targets against these pathogens.
 Aim: The present study employs virtual screening method to identify the protein network interaction of berberine with red complex pathogens.
 Materials and Methods: Computational tools were used to identify the targets, assess their functional role and virulence property. Further, the peptide epitopes present in the virulence factors were identified using the BepiPred tool. The subcellular location of the virulence proteins was also elucidated using PSORTb.
 Results: Berberine was found to target vital protein transporters such as TetR family transcriptional regulator and MerR family transcriptional regulator, which is known to play a crucial role in the survival of bacterial cells.
 Conclusion: Hence the present study provides preliminary data on the protein targets of berberine against red complex pathogens. However, in vitro studies using the compound is warranted to further confirm the efficacy of the compound.

Virtual Screening of the Flavonoids Compounds with the SARS-CoV-2 3C-like Protease as the Lead Compounds for the COVID-19

As of 1st of September 2020, the COVID-19 pandemic has reached an unprecedented level of more than 25 million cases with more than 850,000 deaths. Moreover, all the drug candidates are still undergoing testing in clinical trials. In this regard, a breakthrough in drug design is necessary. One strategy to devise lead compounds is leveraging natural products as a lead source. Several companies and research institutes are currently developing anti-SARS-CoV-2 lead from natural products. Flavonoids are well known as a class of antiviral compounds library. The objective of this research is to employ virtual screening methods for obtaining the best lead compounds from the library of flavonoid compounds. This research employed virtual screening methods that comprised of downloading the protein and lead compound structures, QSAR analysis prediction, iterations of molecular docking simulation, and ADME-TOX simulation for toxicity prediction. The QSAR analysis found that the tested compounds have broad-spectrum antiviral activity, and some of them exhibit specific binding to the 3C-like Protease of the Coronavirus. Moreover, juglanin was found as the compound with the fittest binding with the Protease enzyme of SARS-CoV-2. Although most of the tested compounds are deemed toxic by the ADME-Tox test, further research should be conducted to comprehend the most feasible strategy to deliver the drug to the infected lung cells. The juglanin compound is selected as the fittest candidate as the SARS-CoV-2 lead compound in the tested flavonoid samples. However, further research should be conducted to observe the lead delivery method to the cell.

Role of molecular dynamics in optimising ligand discovery: Case study with novel inhibitor search for peptidyl t-RNA hydrolase

High-throughput virtual screening methods are known to play key roles in drug discovery. However, the computational docking approaches are often blamed for delivering false-positives in the predicted molecules. Here we demonstrate that incorporation of receptor/ligand dynamics in the computational drug discovery approaches can be instrumental in removing false-positives. In particular, we consider a protein with no well-known inhibitor, Peptidyl-tRNA hydrolase (PTH) and show that a combined protocol of ensemble docking and Molecular Dynamics simulation can efficiently screen out a large set of approximately 1600 candidate molecules. Finally, the addition of metadynamics enhanced sampling technique can assist in probing the resilience of ligands against unbinding and in turn provide a re-ranked selection which can be used for synthetic priority. Overall, the investigation recommends the usage of dynamics in a computational drug discovery program for judging their efficacy.

In Silico Drug Design: Development of New Pyrimidine-based Benzothiazole Derivatives, Selective for CDK2

Backgroud: Kinases are proteins that control many biological functions. They are involved in cellular regulation, and many of them are deregulated in cancer proliferation. The evidence of this deregulation in many pathologies served as the origin of kinases as a therapeutic class and constitutes the motive that leads numerous teams to search for inhibitors of these targets. Objective: Based on 3D-QSAR studies and the molecular docking approach, we have developed new potential inhibitors that could be optimized and transformed into colon cancer drugs. Methods: To design new bioactive molecules and study their interactions with the cyclin-dependent kinase type 2 (CDK2) enzyme, we used two virtual screening methods: 3D-QSAR modeling and molecular docking on a series of 28 pyrimidine-based benzothiazole derivatives. Results: To develop the model (3D QSAR), we used CoMFA and CoMSIA techniques using SYBYLX2.0 molecular modeling software. The statistical parameters reveal that the good CoMFA model displays Q² = 0.587 and R²= 0.895 and CoMSIA displays Q² = 0.552 and R² = 0.768), which are considered to be very good internal prediction values, while an external validation of a test series of 5 compounds not included in the model development series gives R² test values of 0.56 for CoMFA and R² t est values of 0.51 for CoMSIA. The molecular docking approach with AutoDock Tools-1.5.6 is introduced in this work to enrich the interpretations extracted from the CoMFA and CoMSIA contour maps and to provide an in silico research method for the most favorable mode of interaction of an inhibitor within its receptor (CDK2). Conclusion: We have constructed and validated a quantitative 3D model of structure-activity relationships of pyrimidine-based benzothiazole derivatives as CDK2 inhibitors. This model allows us to identify the nature and position of the groups that enhance the activity, giving us directions to discover new, more powerful molecules in a limited time.

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.