EXPLORING THE ANTI-CANCER POTENTIAL OF PHYTOCHEMICALS FROM SPECIFIC PLANTS: EXAMINING AND VALIDATING THROUGH MOLECULAR DOCKING AND MD SIMULATIONS
Bibliographic record
Abstract
Worldwide, cancer is the leading cause of death. Anti-cancer medications frequently induce side effects and multidrug resistance (MDR), which continues to be a key obstacle to effective cancer therapy. Essential nutrients and functionally bioactive substances can both be found in abundance in plants. The phytochemical components have great promise for treating both plant and human ailments. This study is designed to conduct an in-silico analysis on phytochemicals derived from Combretaceae family plants for targeting the proteins 4UWH, 5LWM, and 6P3D. The Combretaceae family has demonstrated pharmacological benefits such as anti-leishmanial, cytotoxic, antibacterial, antidiabetic, antiprotozoal, anticancer, and antifungal qualities. To conduct experiments with the natural phytochemicals against the proteins, computerized tools, online servers, and online databases were used. 196 natural compounds were used for virtual screening out of the top 5 best-docked compounds selected based on their binding energy. The best-selected phytochemicals possessed potential results in 10ns molecular dynamic simulation. So, it is convincible based on in-silico research this selected phytochemical has the potential to serve as a promising lead compound against cancer.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".