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Record W4404710468

Molecular Docking of Fisetin as a Multi-target drug in the treatment of Multiple Sclerosis

2022· article· en· W4404710468 on OpenAlexaboutno aff
R. Malathi, Vailina Dsouza, Puja Puja, R. Rithika, P. Sneha

Bibliographic record

VenueCyberLeninK (CyberLeninka) · 2022
Typearticle
Languageen
FieldMedicine
TopicFlavonoids in Medical Research
Canadian institutionsnot available
Fundersnot available
KeywordsFisetinMultiple sclerosisDrugDocking (animal)PharmacologyComputational biologyMedicineChemistryBiologyImmunologyFlavonoidBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Multiple Sclerosis (MS), is an autoimmune disorder of the CNS a long-lasting disorder that can attack the brain, spinal cord, and eyes. The severity of this disorder varies from person to person. Demyelination and lesion formation is the major pathological changes of MS, due to which there is no significant generation of the action potential along the axon of the nerves. In turn, leads to delayed propagation and perception of the chemical signals required for varieties of function in the body. Several theories have been emerging to apprehend the origin of MS such as genes, smoking, viral infection caused by Epstein-Barr virus, or the human herpes virus may trigger the disorder or cause relapses, Vitamin D deficiency etc. Statistical reports show Canada is the country having the highest rate with 1 in 400 people suffering from MS. In the present study, the drug targets of MS were analyzed by understanding its interaction with a plant flavonoid Fisetin having neuroprotective properties. Molecular docking of plant flavonoid fisetin with the enzyme targets of MS was performed using auto-dock 4.2. The minimum binding energy obtained from docking explains the efficiency of the ligand binding with the therapeutic target proteins. Three proteins were selected based on their action and function they play in the progression of Multiple sclerosis namely Caspase 1 (PDB Id: 1lBC), Calpain-1(PDB Id: 2ARY), and Cathepsin B (PDB Id: 1GMY). The docking of fisetin with Caspase 1 (PDB Id: 1lBC), Calpain-1(PDB Id: 2ARY), and Cathepsin B (PDB Id: 1GMY) displayed the minimum binding energy score as, Cathepsin B = -10.01 kcal/mol, Calpain-1= -9.95 kcal/mol, Caspase 1= -8.18 kcal/mol respectively and also the number of hydrogen bonds: 20, 18, 23 respectively. The target proteins Cathepsin B and Calpian 1, showed the strongest interaction with Fisetin with the least minimum binding energy. Molecular properties & drug-likeness, biological activity, and toxicity of Fistein were analyzed using the Way2drug bio tool. Lipinski's benchmark rule of five (RO5)5 defines desirable drug candidate physicochemical property which was successfully shown by the ligand fistein : log P:1.35, HBD: 4, HBA:6 The following are the obtained results showing biological activity and toxicity of ligand fistein: • Biological Activity: 0.966- Membrane intergrity; 0.959- Aryl-alcohol dehydrogenase (NADP+) inhibitor; 0.950- kinase inhibitor • Toxicity: Vascular toxic (0.755 pa), ulcer, aphthous (0.713 pa). where pa [pharmacologically active] is greater than 0.7.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.321
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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