Retrieval of promiscuous natural compounds using multiple targets docking strategy: A case study on kinase polypharmacology
Bibliographic record
Abstract
Cancer is a class of diseases characterized by out-of-control cell growth, which are the building blocks of the body. Imatinib, known by its brand-Gleevec, is a type of biological therapy called tyrosine kinase inhibitor (TKI) which, a chemical messenger, is protein that cells use to signal each other to grow and thus pro-motes cancer. Structure-based method includes inverse docking was used to anticipate of most probable protein targets of Imatinib from tyrosine kinase protein using in silico approaches. Seven tyrosine kinase proteins have been preferred for the docking evaluation. In which, re-docking was performed to evaluate the docking validation. Among them, Crystal structure of native c-Kit kinase in an auto inhibited conformation (PDB: 1T46), LCK bound to imatinib (PDB: 2PL0), P38 in complex with Imatinib/Transferase (PDB: 3HEC), and ABL kinase in complex with Imatinib and a fragment (FRAG1) in the myristate pocket (PDB: 3MS9) and were most potential protein targets for the Imatinib ligand which computed by both of these schemes. These validated proteins have been selected for the virtual library screening of 1500 natural compounds from NPACT database. Luxenchalcone, Schweinfurthin and Sanggenon M were the best docked ligands and have chosen for the understanding the plausible mechanism at molecular level by implying the molecular dynamics simulations to determine the conformational changes and stabilization which reveals the potency of these ligands towards the treatment of cancer treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".