Molecular Docking Analysis of Chloroquine and Hydroxychloroquine and Design of Anti-SARS-CoV2 Protease Inhibitor
Bibliographic record
Abstract
In this present investigation, simulated molecular docking study of chloroquine and hydroxychloroquine compounds were investigated on the SARS-CoV2 enzyme to determine the types of amino acids responsible for the biochemical reaction at the binding site. A structure-based docking design technique was explored in designing a novel derivative of chloroquine for the treatment and management of new COVID 19 disease. To achieve this, the molecular docking simulation method was used to investigate the level of chloroquine and hydroxychloroquine (Drugs presently under clinical trial) interactions on SARS-CoV2 enzyme (a causative agent of COVID 19 disease). Chloroquine and hydroxychloroquine which has been debated as drugs for the management of COVID 19 were subjected to molecular docking analysis, and the binding energies generated were found to be -6.1 kcal/mol and -6.8 kcal/mol respectively. Moreover, novel 2-((4-((7-chloroquinolin-4 yl) amino)pentyl)((methylamino)methyl)amino) ethan-1-ol as an anti-SARS-CoV2 protease was designed through the structural modification of hydroxychloroquine. The binding energy of this drug candidate was found to be -6.9 kcal/mol. This novel drug was found to formed hydrogen and conventional interactions with the binding site of SARS-CoV2 protease through amino acids such as Glutamic acid (GLU166), Glycine (GLY143), Phenylalanine (PHE140), Asparagine (ASN142), Histidine (HIS163), His (HIS172, HIS41, HIS163), Leucine (LEU41, LEU27), Glycine (GLY143), Glutamine (GLN189), Methionine (MET49, MET165), Serine (SER 46), Cysteine (CYS145) and Threonine (THR25). With this binding energy, this new drug candidate could bind better to the human SARS-CoV2 protease’ binding site. This research provides a clue for other scientists on various ways of designing and identify the types of amino acids that may be responsible for biochemical action on SARS-CoV2 protease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".