The new coronavirus SARS-COV-2, which emerged in late 2019 from Wuhan city of China was regarded as causing agent of the COVID-19 pandemic. The primary protease which is also known by various synonymous i.e., main protease, 3-Chymotrypsin-like protease (3CLPRO) has a vital role in the replication of the virus, which can be used as a potential drug target. The current study aimed to identify novel phytochemical therapeutics for 3CLPRO by machine learning-based virtual screening. A total of 4,000 phytochemicals were collected from deep literature surveys and various other sources. The 2D structures of these phytochemicals were retrieved from the PubChem database, and with the use of a molecular operating environment, 2D descriptors were calculated. Machine learning-based virtual screening was performed to predict the active phytochemicals against the SARS-CoV-2 3CLPRO. Random forest achieved 98% accuracy on the train and test set among the different machine learning algorithms. Random forest model was used to screen 4,000 phytochemicals which leads to the identification of 26 inhibitors against the 3CLPRO. These hits were then docked into the active site of 3CLPRO. Based on docking scores and protein-ligand interactions, MD simulations have been performed using 100 ns for the top 5 novel inhibitors, ivermectin, and the APO state of 3CLPRO. The post-dynamic analysis i.e,. Root means square deviation (RMSD), Root mean square fluctuation analysis (RMSF), and MM-GBSA analysis reveal that our newly identified phytochemicals form significant interactions in the binding pocket of 3CLPRO and form stable complexes, indicating that these phytochemicals could be used as potential antagonists for SARS-COV-2.

https://www.frontiersin.org/articles/10.3389/fmolb.2023.1060076/pdf

Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation

In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program’s NCI60 data (22,726 structures) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.

https://www.mdpi.com/1422-0067/24/2/1689/pdf?version=1674042952

CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds

Introduction Monkeypox is a zoonotic disease caused by brick-shaped enveloped monkeypox (Mpox) virus that belongs to the family of ancient viruses known as Poxviridae. Subsequently, the viruses have been reported in various countries. The virus is transmitted by respiratory droplets, skin lesions, and infected body fluids. The infected patients experience fluid-filled blisters, maculopapular rash, myalgia, and fever. Due to the lack of effective drugs or vaccines, there is a need to identify the most potent and effective drugs to reduce the spread of monkeypox. The current study aimed to use computational methods to quickly identify potentially effective drugs against the Mpox virus. Methods In our study, the Mpox protein thymidylate kinase (A48R) was targeted because it is a unique drug target. We screened a library of 9000 FDA-approved compounds of the DrugBank database by using various in silico approaches, such as molecular docking and molecular dynamic (MD) simulation. Results Based on docking score and interaction analysis, compounds DB12380, DB13276, DB13276, DB11740, DB14675, DB11978, DB08526, DB06573, DB15796, DB08223, DB11736, DB16250, and DB16335 were predicted as the most potent. To examine the dynamic behavior and stability of the docked complexes, three compounds—DB16335, DB15796, and DB16250 —along with the Apo state were simulated for 300ns. The results revealed that compound DB16335 revealed the best docking score (-9.57 kcal/mol) against the Mpox protein thymidylate kinase. Discussion Additionally, during the 300 ns MD simulation period, thymidylate kinase DB16335 showed great stability. Further, in vitro and in vivo study is recommended for the final predicted compounds.

/pdf/computer-assisted-drug-repurposing-for-thymidylate-kinase-1nw00shy.pdf

Computer-assisted drug repurposing for thymidylate kinase drug target in monkeypox virus

In this study, twenty eight novel oxadiazole derivatives (5–32) of the marketed available non-steroidal anti-inflammatory drug (NSAID), (S)-flurbiprofen (1), were synthesized via I2 mediated cyclo-addition reaction in better yields. The synthesized hydrazone-Schiff bases were cyclized with iodine by using potassium hydroxide as a base in DMSO solvent to obtain oxadiazole derivatives (5–32). Structures of the synthesized products were confirmed with HR-ESI-MS, 1H-NMR spectroscopy and CHN analysis. After structure confirmations all analogs were evaluated for urease (in vitro) inhibitory activity. Amongst the series, fourteen compounds 20, 26, 30, 24, 21, 16, 28, 31, 32, 7, 19, 13, 10, and 6 were found to be excellent inhibitors of urease enzyme, having IC50 values of 12 ± 0.9 to 20 ± 0.5 μM, better than the standard thiourea (IC50 = 22 ± 2.2 μM), whereas the remaining fourteen derivatives displayed good to moderate activity. The in silico study was executed to analyse the interaction between the active site of the enzyme (urease) and the produced compounds. The docking study revealed that compounds 20, 26, 30, 24, 21, 16, 28, 31, 32, 7, 19, 13, 10, and 6 had lower docking scores than the standard compound thiourea and revealed better interactions with the urease enzyme.

Novel flurbiprofen clubbed oxadiazole derivatives as potential urease inhibitors and their molecular docking study

Identification of novel STAT3 inhibitors for liver fibrosis, using pharmacophore-based virtual screening, molecular docking, and biomolecular dynamics simulations

BACKGROUND
Signal transducers and activators of transcription (STAT) family consist of seven structurally similar and highly conserved members, including STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6. The STAT3 signaling cascade is activated by upstream kinase signals and undergoes phosphorylation, homo-dimerization, nuclear translocation, and DNA binding, resulting in the expression of target genes involved in tumor cell proliferation, metastasis, angiogenesis, and immune editing. STAT3 hyperactivation has been documented in a number of tumors, including head and neck, breast, lung, liver, kidney, prostate, pancreas cancer, multiple myeloma, and acute myeloid leukemia. Drug discovery is a time-consuming and costly process, to bring a single drug to the market, may take ten to fifteen years. Machine learning algorithms are very fast as well as effective that are commonly used in the field such as drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive.


OBJECTIVE
The present work aims to perform machine learning based virtual screening for STAT3 drug target.


METHODS
Machine learning models such as k-nearest neighbor, support vector machine, Gaussian naïve Bayes, and random forest for classifying the active and inactive inhibitors against a STAT3 drug target were developed. Ten-fold cross-validation was used for model validation. Then the test dataset prepared from the zinc database was screened using the random forest model. Total of 20 compounds with 88% accuracy was predicted as active against STAT3. Furthermore, these twenty compounds were docked into the active site of STAT3. The two complexes with good docking scores as well as the reference compound were subjected to MD simulation. A total of 100ns MD simulation was performed.


RESULTS
Compared to all other models, random forest model revealed better results. Compared to the standard reference compound, the top two hits revealed greater stability and compactness.


CONCLUSION
In conclusion, our predicted hits have the ability to inhibit STAT3 overexpression to combat the STAT3 associated diseases.

Machine learning-based virtual screening for STAT3 anticancer drug target.

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Machine learning-based virtual screening for STAT3 anticancer drug target.