Plasmodium falciparum is the leading cause of malaria with 627,000 deaths annually. Invasion and egress are critical stages for successful infection of the host yet depend on proteins that are extensively pre-processed by various maturases. Plasmepsins (Plasmodium pepsins, abbreviated PM, I-X) are pepsin-like aspartic proteases that are involved in almost all stages of the life cycle. The goal of this study was to use de-novo generative modeling techniques to create novel potential PfPMX inhibitors. A total of 4325 compounds were virtually screened by structural-based docking methods. The obtained hits were utilized to refine a structure-based Ligand Neural Network (L-Net) generative model to generate related compounds. The obtained optimal L-Net Compounds with smina scores ≤ -5.00KCalmol-1 and QED ≥ 0.35 were further taken for amplification utilizing Ligand Based Transformer modeling using Deep generative learning (Drug Explorer/DrugEx). The resulting hits were then subjected to XP Glide conventional Molecular docking and QikProp ADMET screening; molecules with XP Docking score ≤ -7.00KCalmol-1 were retained. Based on their Glide ligand efficiency, originality, and uniqueness, 30 compounds were chosen for binding affinity and MM_GBSA energy determination. Following Induced Fit docking (IFD), 7 compounds were taken for 50 ns MD simulations and FEP/MD calculations. This study reported novel potential PfPMX inhibitors with acceptable ADMET profiles and reasonable synthetic accessibility scores, as well as sufficient docking scores against other PMs were generated. The PfPMX inhibitors reported in this article are promising antimalarials for the next stages of drug development, and the first of their kind to be investigated thoroughly.Communicated by Ramaswamy H. Sarma.

Artificial intelligence based de-novo design for novel Plasmodium falciparum plasmepsin (PM) X inhibitors.

Abstract Cases of the monkeypox virus have been recorded in non-endemic nations and have continued to be reported in several endemic nations since early May 2022 [1]. In this study, we modeled MPXV Thymidylate Kinase (TMPK) and scaffolding protein (D13), and these models and their templates were taken for small molecule screening against 602,413 small molecules using pharmacophore modeling and molecular docking methods. ADMET properties were also computed followed by Molecular simulation dynamics studies. All presented hits had superior molecular docking scores to used reference standards of Cidofovir and Rifampicin. TMPK compounds displayed better ADMET profiles than D13 compounds, hence the latter may necessitate optimization. Following molecular dynamics simulation, calculations of the RMSDs, RMSFs, Hydrogen Bonds, SASA, Radius of Gyration, MM-GBSA energy, PCA, and DCC revealed that all generated complexes were stable, with the ligands NPC275538, NPC244454, 135566871, and CHEBI compounds outperforming other Hits. These compounds still presented higher docking scores against cidofovir-resistant TMPK and Rifampicin-resistant D13 proteins. Compounds 447970, 446595, and 54723327 were most selective against human TMPK. The conserved interaction patterns of these compounds among Vaccinia and Monkeypox virus proteins with the fact that studied proteins are highly conserved across Orthopoxviruses (OPV) is appealing to the fact these hits should be studied across OPV. Therefore, these compounds should be subjected to laboratory testing to prove their antipox capability. Since there are currently no approved MPXV antivirals, this discovery significantly aids in developing new drugs for treating monkeypox. 

The Hunt for antipox compounds against Monkeypox Virus Thymidylate Kinase and scaffolding protein leveraging Pharmacophore modeling, molecular docking, ADMET Studies, and molecular dynamics simulation studies

Abstract RSV remains a burden on healthcare systems and society despite medical advancements. For over 60 years, developing a vaccine for RSV has been a top priority, but efforts have been ineffective. The physical interactions between Nucleolin (NCL) RBD1,2 and RSV-F protein during virus entry were identified by peptide arrays as two antiparallel strands of β-sheet in RBD1. I screened for novel NCL-targeting ligands to inhibit this interaction. In a nutshell, small molecule inhibitors and nucleic acid aptamers were specifically docked to NCL RBD1, 2. A total of 528 aptamers were docked to NCL RBD1,2 on the HDOCK website; the top 180 aptamers with docking scores equal to or better than -200 were then re-docked at the HADDOCK website, and the top 58 aptamers were chosen. Additionally, 976,450 small molecules were docked using Schrodinger Virtual Screening Workflow, and the top 50 ligands underwent post-docking validation using Prime MM_GBSA. The 16 best ligands were chosen from the top 30 using binding affinity from Molegro Virtual Docker. For molecular simulation dynamics studies, four protein-ligand complexes and six protein-aptamer complexes were randomly chosen and the results were analyzed. The results show that the selected NCL ligands are potential NCL binders and can inhibit NCL-RSV-F interactions. Results suggest that aptamers are better binders than small molecules. BDBM50308336, 1exd, and 5uza formed the most stable complexes. With many benefits as host-directed therapeutics, these ligands need in-vitro testing for potential inhibition of RSV entry besides some probable drawbacks. 

Host-Directed anti-fusion aptamers and small molecules as Respiratory Syncytial Virus (RSV) inhibitors: An Insilco-based study.

Host-Directed Anti-Fusion Aptamers and Small Molecules as Respiratory Syncytial Virus (Rsv) Inhibitors: An Insilco-Based Study

Abstract Introduction: The rate of acute hepatitis C increased by 7% between 2020 and 2021, after the number of cases doubled between 2014 and 2020. With the current adoption of pan-genotypic HCV therapy, there is a need for improved availability and accessibility of this therapy. However, double and triple DAA-resistant variants have been identified in genotypes 1 and 5 with resistance-associated amino acid substitutions (RAASs) in NS3/4A, NS5A, and NS5B 1 . The role of this research was to screen for novel potential NS5B inhibitors from the cannabis compound database (CBD) using Deep Learning. Methods Virtual screening of the CBD compounds was performed using a trained Graph Neural Network (GNN) deep learning model. Re-docking and conventional docking were used to validate the results for these ligands since some had rotatable bonds &gt; 10. 31 of the top 67 hits from virtual screening and docking were selected after ADMET screening. To verify their candidacy, six random hits were obtained for FEP/MD and Molecular Simulation Dynamics. Results The top 200 compounds from the deep learning virtual screening were selected, and the virtual screening results were validated by re-docking and conventional docking. The ADMET profiles were optimal for 31 hits. Simulated complexes indicate that these hits are likely inhibitors with suitable binding affinities and FEP energies. Phytil Diphosphate and glucaric acid were suggested as possible ligands against NS5B. 

Geometric Deep learning Prioritization and validation of cannabis phytochemicals as anti-HCV non- nucleoside direct-acting inhibitors

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late 2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to health and the general public safety. We performed deep docking studies of 800 M unique compounds in both the active and allosteric sites of the SARS-COV-2 Main Protease (Mpro) and the 15 M and 13 M virtual hits obtained were further taken for conventional docking and molecular dynamic (MD) studies. The best XP Glide docking scores obtained were -14.242 and -12.059 kcal/mol by CHEMBL591669 and the highest binding affinities were -10.5 kcal/mol (from 444215) and -11.2 kcal/mol (from NPC95421) for active and allosteric sites, respectively. Some hits can bind both sites making them a great area of concern. Re-docking of 8 random allosteric complexes in the active site shows a significant reduction in docking scores with a t-test P value of 2.532 × 10-11 at 95% confidence. Some specific interactions have higher elevations in docking scores. MD studies on 15 complexes show that single-ligand systems are stable as compared to double-ligand systems, and the allosteric binders identified are shown to modulate the active site binding as evidenced by the changes in the interaction patterns and stability of ligands and active site residues. When an allosteric complex was docked to the second monomer to check for homodimer formation, the validated homodimer could not be re-established, further supporting the potential of the identified allosteric binders. These findings could be important in developing novel therapeutics against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.

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Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.