Journal of Computational Biophysics and Chemistry

The ongoing eruption of the COVID-19 pandemic instigated by severe-acute-respiratory-syndrome-coronavirus 2 (SARS-CoV-2) has produce enormous damage to the world. The need of the hour is to stop this pandemic by inhibiting the main protease (M^Pro) of SARS-CoV-2, which is primarily involved in viral replication. Our study aims to find potential inhibitors for M^Pro by docking marine fungi-based 90 antiviral compounds against SARS-CoV-2. Among these, 11 antiviral compounds (obeying Lipinski RO5) are selected from 90 docked antiviral compounds on the basis of binding energy range ($-$6.4kcal/mol to $-$9kcal/mol) and low inhibition constant values (0.23$\mu$M to 2.5$\mu$M) as compared with remdesivir (reference compound) toward M^Pro of SARS-CoV-2. Tryptoquivaline F, arisugacin B, and arisugacin A antiviral compounds exhibited effective hydrogen and hydrophobic (alkyl, $\pi$-alkyl, and $\pi$-anion) interactions and are expected to be potential protease inhibitors. Drug-likeness of these lead compounds are elaborated by boiled-egg and bioavailability radar map. The toxicity profile showed that the lead compounds L1, L2, and L3 have no AMES toxicity, skin sensitization, and cardiac toxicity. The RMSD graph proposed that all the complexes, i.e. L1, L2, and L3 are in the adequate RMSD range with the average value of 2.1Å. All the complex systems of L1, L2, and L3 showed fluctuations in the acceptable RMSF range of 1.5Å to 3Å. The molecular dynamics simulation proved the stability of docked complexes L1, L2, and L3 in the binding pocket of main protease. The average hydrogen count of all complexes is $\mathrm{\text{L1}}=69.5$, $\mathrm{\text{L2}}=67.7$, and $\mathrm{\text{L3}}=68.6$ H-bonds. The complexes L1-M^Pro, L2-M^Pro, and L3-M^Pro have an average value of $R_g$ as 22.44Å, 22.63Å, and 22.50Å, respectively. The lead compounds L1 (tryptoquivaline F), L2 (arisugacin b), and l3 (arisugacin A) in this study are the most promising inhibitors of SARS-CoV-2 main protease M^Pro, which are not reported in ealier studies. Our findings will evoke the scientific interest for their further in vitro and in vivo experimental studies.

In this paper, a method of deducting activation energies for heterogeneous reactions of steam methane reforming is presented. The essence of the method lies in iterative evaluation of kinetic parameters, namely activation energies of reactions, for a given reactor. The novelty of the method lies in utilizing a statistical approach to reduce computational effort of numerical simulation. The method produces multivariable correlations between activation energies and operational parameters of the process: pressure, temperature, steam-to-methane ratio, residence time, and catalyst properties. These correlations can be used for numerical simulations of steam methane reforming to yield methane conversion rate, spatial and temporal distribution of reaction products, temperature and pressure within the reactor. An average computational effort is equal to a batch of 18 ($3n+1$) simulations for $n$ variables. The method was demonstrated by evaluating two-variable correlations of activation energies with pressure and temperature. The developed numerical model was validated against adopted experimental data.

Reactive free radicals have both beneficial and destructive effects. Indeed, at physiological levels, free radicals help to preserve homeostasis by acting as signal transducers. However, excessive generation of free radicals can harm and damage membranes, proteins, and DNA, among other cell structures. Dihydroxybenzoic acids (DHBAs) have proven their antioxidant capacity against a large variety of free radicals, as well as their ability to inhibit or restrict reactive species overproduction. In this paper, a computational analysis of the antioxidant activity of a series of DHBAs in polar and nonpolar media was carried out at the DFT/M06-2X/6-$311++G(d,p)$ level of theory. The implicit SMD solvation model was used in order to rationalize the experimental findings and to investigate the solvent effect on the mechanism and the radical scavenging ability. The obtained results put in evidence that HAT is the predominant mechanism in nonpolar media, whereas SPLET is more favored in polar environment. The BDE${}_{min}$, ${(\mathrm{\text{PA}}+\mathrm{\text{ETE}})}_{min}$, and ${(\mathrm{\text{IP}}+\mathrm{\text{PDE}})}_{min}$ descriptors are used to predict the most reactive hydroxyl groups and the antioxidant activity order of the studied DHBAs. Our results are in total agreement with experimental findings (inhibition of lipid peroxidation and scavenging of hydrogen peroxide). Moreover, this study shows that the substitution of the hydrogen atom by strong electron-donating groups, namely NMe₂, in the ortho positions of the best experimental DHBAs leads to a significant enhancement of their antioxidant activity.

Breast cancer is the most common cancer among women worldwide. Breast cancer is caused by the overexpression of genes that facilitate breast cell proliferation. Estrogen receptor alpha (ER$\alpha$) mediation is mostly responsible for the development of malignant tumors by regulating the transcription of various genes as a transcription factor. ER$\alpha$ is regarded as an important receptor for the proliferation of this disease and its inhibition is necessary for the treatment of breast cancer. In our study, the aim is to find out potential inhibitors for ER$\alpha$ by docking 100 anticancer constituents of different halogen-based derivatives against ER$\alpha$. Among the 100 $X$-based derivatives, 20 ligands were selected based on the interaction energy ranging from $-$6.7kcal/mol to $-$8.2kcal/mol and lower values of inhibition constant (0.92–11.77$\mu \mathrm{\text{mol}}$). We have performed a comprehensive analysis of five most popular ligands (L17, L57, L61, L67, and L70) among these 20 selected derivatives. The interaction analysis is mainly stabilized by making different interactions including hydrogen bonding, hydrophobic, electrostatic, and halogen bonding with the active site of ER$\alpha$. A quantum study based on frontier molecular orbitals (FMOs), molecular electrostatic potential (MEP), and global chemical reactivity descriptor (GCRD) is carried out to explore the structural chemistry of our most popular ligands which indicates that they are quite reactive and kinetically stable. Additionally, we also assess the ADMET profiles to predict the toxicity and drug-likeness of our lead compounds. Our selected ligands have the highest absorption and good clearance rate and have no AMES toxicity, skin sensitization, and hepatotoxicity. Molecular dynamics simulations were run to check the conformational stability of apo form of ER$\alpha$ and complex state, at 60-ns time scale. The molecular dynamics simulations are based on the plots of RMSD, RMSF, $R_g$, SASA, and the number of H-bonds. The results of RMSD and RMSF showed that the lead compounds L17, L57, L61, L67, and L70 are most stable and have no significant residual fluctuations and deviation. The analysis of the plots of $R_g$, SASA, and the number of H-bonds revealed that the complexes L17, L57, and L70 are most stable. So, the lead anticancer compounds L17, L57, L61, L67, and L70 are the most promising inhibitors against ER$\alpha$ of breast cancer. The comprehensive analysis of all studied parameters highlighted that our selected ligands have great potential to inhibit ER$\alpha$. So, it can be concluded that the selected halogen-based derivatives can promote rational drug design for the target therapies of ER$\alpha$ of breast cancer.

The past few years have witnessed machine learning techniques take the limelight in multiple research domains. One such domain that has reaped the benefits of machine learning is computer-aided drug discovery, where the search space for candidate drug molecules is decreased using methods such as virtual screening. Current state-of-the-art sequential neural network models have shown promising results and we would like to replicate similar results with virtual screening using the encoded molecular information known as simplified molecular-input line-entry system (SMILES). Our work includes the use of attention-based sequential models — the long short-term memory with attention and an optimized version of the transformer network specifically designed to deal with SMILES (ChemBERTa). We also propose the “Overall Screening Efficacy”, an averaging metric that aggregates and encapsulates the model performance over multiple datasets. We found an overall improvement of about $27\%$ over the benchmark model, which relied on parallelized random forests.

Coumarin derivatives are broadly used as anti-inflammatory, antioxidants, anticancer, and antiviral drugs in recent years. In particular, hydroxy coumarins have great importance because of their various biological and pharmacological purposes. The quantum chemical studies of 4,7-dihydroxycoumarin (DHC) have been performed using the cc-pVTZ level of basis set. The DHC molecular structure has been optimized and the computed frequency assignments have been correlated well with the experimental results. The experimental ${}^{13}$C NMR shifts of DHC have been compared with the computed ${}^{13}$C NMR in the dimethyl sulfoxide (DMSO) solution using the Gauge-invariant atomic orbital (GIAO) method. The electron delocalization within the DHC is shown by highest occupied molecular orbitals (HOMO)-lowest unoccupied molecular orbitals (LUMO) energy analysis, and the resulting small energy gap value reveal the molecule’s bioactive characteristics. The natural bond orbital (NBO) analysis approves the bioactive property of the DHC molecule. The DHC compound has a cytotoxic impact on the MCF-7 breast cancer cell line, according to in vitro cytotoxicity studies. The docking study approves that the DHC works as a new inhibitor of breast cancer targeted proteins such as epidermal growth factor receptor (EGFR), estrogen receptor (ER), and progesterone receptor (PR). Thus, this work covers the approach for the evolution of new drugs against breast cancer.

Over 200 million people worldwide are affected annually by schistosomiasis with debilitating socio-economic effects. Praziquantel remains the main chemotherapy against this neglected tropical disease but there are reports of drug resistance. Therefore, necessitating the need to identify potential biotherapeutic molecules. The study describes the first deep learning (DL)-based computational models for predicting inhibitors of Schistosoma mansoni Thioredoxin glutathione reductase (SmTGR), which is an essential protein for the survival of the helminths in the host. The state-of-the-art performance of DL in similar applications makes it ideal to deploy on bioactive datasets of the SmTGR drug target. Cost-sensitive deep neural network classifiers were trained using the binary classification approach. Based on the area under curve (AUC) of the receiver operating characteristic (ROC) Curve scores (86.3–86.5%), the five best models generated were able to classify inhibitors with high accuracy (85–90%). Additionally, the DL classifier outperformed random forest by far. This is a proof of concept that deep neural networks can efficiently and robustly classify active schistosomal molecules from inactive. The generated models could be used to screen large-scale compound libraries to prioritize potential inhibitors for experimental characterization.

According to the antimicrobial activity of 1,3,4-thiadiazole compounds, a new series of derivatives was synthesized from the reaction of 5-nitro heteroaryl-1,3,4-thiadiazole with piperazinyl benzonitrile. The structure of the compounds was confirmed using Fourier transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance (¹HNMR), and mass spectral data. The antimicrobial activities of the synthetic compounds against bacterial strains (Staphylococcus aureus and Escherichia coli) and fungal strains (Candida albicans and Saccharomyces Cerevisiae) showed compound E as the best antibacterial compound. After that, the molecular docking of the compounds with Escherichia coli dihydroorotase (PDB ID: 2EG7) and Klebsiella pneumoniae (PDB ID: 4OR7) was studied using ArgusLab software, which showed low binding energies $\le -6.07$Kcal/mol and $-7.17$Kcal/mol, respectively. Finally, the prediction of their drug-likeness by the assessment of some physical and chemical properties such as hydrogen bond donor/acceptor properties, water solubility, lipophilicity and acute oral toxicity acknowledged their drug-like properties.