Ishola Abeeb Akinwumi

Here, we present MolPic, an open-source Python-based software that can be used to generate high-resolution, publication-quality molecular figures directly from compound names or SMILES strings. MolPic supports single-molecule rendering, batch processing, and automated multi-panel 2D figure generation, which are suitable for manuscripts and presentations. MolPic generates a scalable vector graphics (SVG) image as the output. MolPic is fully compatible with Linux, macOS, and cloud-based environments such as Google Colab. The software is archived with a permanent DOI and is freely available to the scientific community.

Background: Coronavirus disease 2019 (COVID-19) is an infectious disease brought on by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a global treat in early 2020. Despite worldwide research proving different medications used to treat COVID-19, the infection still affects the human race; we need to continue researching the virus to protect humanity and reduce the complications that some medications might cause. This study focuses on finding another promising therapeutic compound against SARS-CoV-2. Twenty-four (24) bioactive compounds were selected from the following African plants' Adansonia digitata L, Aframomum melegueta K. Schum, Ageratum conyzoides (L.) L, and Boswellia dalzielii, and Remdesivir was used as the control medication. The PubChem web server acquired the 3D structures of bioactive compounds in the plant and the control medication. The SARS-CoV-2 main protease (M^pro) crystal structure was obtained using the Protein Data Bank (PDB). Using the SwissADME web server, the bioactive compounds' drug-likeness was assessed, and AutoDock was employed for the molecular docking with the M^pro. The Proteins Plus and Protein–Ligand Interaction Profiler web servers were used to analyse the docked complexes. Furthermore, the admetSAR website was utilized to predict the ligands' absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Results: Based on the drug-likeness screening, Rutin violated more than one of the Lipinski rules of five, while Remdesivir violated two. Molecular docking analysis results indicated that Catechin, Epicatechin, Vitexin, Quercetin, Kaempferol, Gamma-Sitosterol, and Kaur-16-ene exhibited a stronger binding affinity with M^pro, with binding scores of − 7.1, − 7.1, − 8.0, − 7.3, − 7.2, − 6.8, and − 6.5 kcal/mol, respectively, compared to Remdesivir's binding score of − 6.3 kcal/mol. Consequently, binding scores of bioactive compounds suggest their potential biological activity against the SARS-CoV-2 main protease. Additionally, these bioactive compounds exhibited favourable ADMET properties. Vitexin also has a plasma protein binding below 90%, a promising medication distribution feature. Conclusions: This study shows that Catechin, Epicatechin, Vitexin, Quercetin, Kaempferol, Gamma-Sitosterol, and Kaur-16-ene have better binding affinities with M^pro than Remdesivir. Molecular dynamics simulation in vitro and in vivo investigation is required to support this study.

Objective: Despite the vast array of approved anti-diabetic drugs and the fact that these medications are often associated with other serious side effects like cardiovascular disease, weight gain, liver disorders, and countless others, the prevalence of Type-2 diabetes is soaring. Through in silico analysis, our study seeks to elucidate the anti-diabetic potential of six (6) medicinal plants Buchholzia coriacea, Vernonia amygdalina, Mimosa pudica, Momordica charantia, Bergenia ciliate, and Mangifera indica. Methods: Twenty-nine (29) bioactive compounds were selected from the six plants. Metformin and Miglitol are used as the control drug in this study. PubChem, an online server, was used to get the 3D structure of the bioactive compounds and the control drugs. The protein data bank was used to retrieve the crystal structure of the SIRT6 protein. The SwissADME online server was used for the Drug-likeness of the bioactive compounds and the control drugs. AutoDock was used for the molecular docking of compounds that passed the drug-likeness with the SIRT6 active site. The protein-ligand complexes were analyzed using a protein-ligand interaction profiler and proteins plus web server. The Molinspiration online server was used to predict compound bioactivity. The ADMETlab webserver was used to determine the ligands' ADMET properties. Results: The drug-likeness screening of the twenty-nine compounds and the control drugs revealed that twenty-five compounds have zero or one violation of Lipinski's rule of five. Metformin and Miglitol have zero violations. The docking analysis revealed that twenty out of the twenty-five compounds docked against the protein target have better binding affinity than the control drugs. Catechin, Luteolin, Chlorogenic acid, and Mimopudine have an excellent binding affinity of -8.4 kcal/mol -7.8 kcal/mol, -7.7 kcal/mol, and -7.5 kcal/mol, respectively. In contrast, Metformin and Miglitol have binding scores of -4.8 and -5.1 kcal/mol, respectively. Conclusions: Therefore, the greater binding affinity of the twenty compounds compared to the control drugs suggests that these compounds possess anti-diabetic properties with good interaction with the SIRT6 protein. However, this research needs further validation with molecular dynamics studies and in-vitro and in-vivo evaluation. 1Department of Chemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia. 2Department of Biomedical Technology, School of Basic Medical Sciences, Federal University of Technology, Akure, Ondo State, Nigeria. 3Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Lagos, Lagos Nigeria. 4Department of Biochemistry, Faculty of Science Laboratory Technology, Federal Polytechnic Ilaro, Ogun State Nigeria 5Department of Chemistry, School of Physical Sciences, Federal University of Technology, Akure, Ondo State, Nigeria.

Objective: This present study aims to assess in silico inhibitory potentials of bioactive compounds present in Vernonia amygdalina (Bitter leaf), Cymbopogon citratus (Lemongrass), Azadirachta indica (Neem leaf), and Carica papaya (Pawpaw leaf) against Plasmodium falciparum Dihydrofolate reductase-thymidylate synthase (pfDHFR-TS) via binding at their active sites. Methods: In silico methods were used in this study. Twenty (20) bioactive compounds were selected from Vernonia amygdalina, Cymbopogon citratus, Azadirachta indica, and Carica papaya. Artemether and Lumefantrine were used as the control drugs. The PubChem identification number (PID), the 3D structure in structure data format (SDF), and the canonical SMILES of the bioactive compounds and the control drugs were obtained using the PubChem online server. The crystal structure of pfDHFR-TS was retrieved from the protein data bank. Drug-likeness of the selected bioactive compounds was assessed using the SwissADME online server. The successful compounds were docked into the protein's active site using AutoDock Vina docking software. The docked complexes were analyzed using proteins plus and protein-ligand interaction profiler web server. The bioactivity of the ligands was determined using the Molinspiration online server. ADMETlab online tool was used to determine the ligands' absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics.Results: The drug-likeness screening indicated that eleven (11) out of the twenty bioactive compounds violated two or more of the five rules (Lipinski's, Ghose's, Veber's, Egan's, and Muegge's rules). The control drug Artemether didn't violate any rule, while Lumefantrine violated four out of the five rules. The molecular docking revealed that Nimbolide, Vernomygdin, Luteolin, and Emetine from Azadirachta indica (Neem leaf), Vernonia amygdalina (Bitter leaf), and Carica papaya (Pawpaw leaf) have binding energies of -10.1 kcal/mol, -9.2 kcal/mol, -8.6 kcal/mol, and -9.2 kcal/mol respectively, which are better than the binding energies of Artemether and Lumefantrine (-8.2 kcal/mol, and -7.6 kcal/mol). Thus, these bioactive compounds' binding energies indicate the binding affinity with pfDHFR-TS protein, suggesting that the bioactive compounds may possess a biological activity against malarial. The best four ligands, Nimbolide, Vernomygdin, Luteolin, and Emetine, also showed excellent ADMET properties. Conclusion: Conclusively, the in silico analysis proposes that Nimbolide, Vernomygdin, Luteolin, and Emetine from Azadirachta indica (Neem leaf), Vernonia amygdalina (Bitter leaf), and Carica papaya (Pawpaw leaf) prove to be probable antimalarial drugs, and show better docking with the target protein compared to Artemether and Lumefantrine. To validate this study, an in-vitro and in vivo study is recommended to further this study for validation of the hit compounds, as in silico methods only predict the activity of these bioactive compounds.