Development of Putative Isospecific Inhibitors for HDAC6 using Random Forest, QM-Polarized docking, Induced-fit docking, and Quantum mechanics
Joel, I. y.; ADIGUN, T. O.; BANKOLE, O. O.; AJIBOLA, A. O.; OFENIFORO, E. B.; AUTA, F. B.; OZOJIOFOR, U. O.; REMI-ESAN, I. A.; AKANDE, A. I.
Show abstract
Histone deacetylases have been recognized as a potential target for epigenetic aberrance reversal in the various strategies for cancer therapy, with HDAC6 implicated in various forms of tumor growth and cancers. Diverse inhibitors of HDAC6 has been developed, however, there is still the challenge of iso-specificity and toxicity. In this study, we trained a Random forest model on all HDAC6 inhibitors curated in the ChEMBL database (3,742). Upon rigorous validations the model had an 85% balanced accuracy and was used to screen the SCUBIDOO database; 7785 hit compounds resulted and were docked into HDAC6 CD2 active-site. The top two compounds having a benzimidazole moiety as its zinc-binding group had a binding affinity of -78.56kcal/mol and -78.21kcal/mol respectively. The compounds were subjected to exhaustive docking protocols (Qm-polarized docking and Induced-Fit docking) in other to elucidate a binding hypothesis and accurate binding affinity. Upon optimization, the compounds showed improved binding affinity (-81.42kcal/mol), putative specificity for HDAC6, and good ADMET properties. We have therefore developed a reliable model to screen for HDAC6 inhibitors and suggested a series of benzimidazole based inhibitors showing high binding affinity and putative specificity for HDAC6.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 98%
- Molecular docking, simulation and binding free energy analysis of small molecules as PfHT1 inhibitors 98%
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 97%
Similar papers in this journal
- In Silico Identification of Potential Inhibitors of Mycobacterium tuberculosis DNA Gyrase from Phytoconstituents of Indian Medicinal Plants 98%
- ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds 95%
- Combining Multi-Dimensional Molecular Fingerprints to Predict hERG Cardiotoxicity of Compounds 95%
Similar papers in this journal
- De novo drug designing coupled with brute force screening and structure guided lead optimization gives highly specific inhibitor of METTL3: a potential cure for Acute Myeloid Leukaemia 98%
- Pharmacophore modeling, 2D-QSAR, Molecular Docking and ADME studies for the discovery of inhibitors of PBP2a in MRSA 96%
- Structural analysis and ensemble docking revealed the binding modes of selected progesterone receptor modulators 96%
Similar papers in this journal
- Elucidation of cryptic and allosteric pockets within the SARS-CoV-2 protease 95%
- Structure-based identification of naphthoquinones and derivatives as novel inhibitors of main protease Mpro and papain-like protease PLpro of SARS-CoV-2 95%
- Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening 95%
Similar papers in this journal
- Structure-Based Design of Small-Molecule Inhibitors of Human Interleukin-6 97%
- In depth analysis of kinase cross screening data to identify CAMKK2 inhibitory scaffolds 96%
- Why Ortho- and Para-Hydroxy Metabolites Can Scavenge Free Radicals That the Parent Atorvastatin Cannot? Important Pharmacologic Insight from Quantum Chemistry 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.