Editorial: Hot trends in computer-aided drug design techniques
Bibliographic record
Abstract
The drug discovery process is complex and designing an effective and commercially viable drug 13 requires interdisciplinary work. For this reason, Computer Aided Drug Design (CADD) Centre works 14 with collaboration between structure biologists, biophysicists, and computational scientists to find new 15 therapeutic agents. The design and development of any medicine takes many years: it begins when 16 scientists learn about a biological target (e.g., a receptor, enzyme, protein, gene, etc.) that is involved 17 in a biological process thought to be dysfunctional in patients with a disease, followed by the 18 determination of specific target receptor, and finally by the determination of active compound from the 19 mass of compounds [1][2][3]. 20In the collection, we focused on the publication of papers that take Computer-assisted approaches such used as a 3D query to screen a drug-like database to retrieve hits with novel chemical scaffolds. The 58 obtained compounds were subjected to binding affinity prediction using the molecular docking 59 approach, and the results were subsequently validated using molecular dynamics (MD) simulations. 60Computer-aided drug design perspective is the review of Dr Rahman et al. Through a computational 62 approach, this study aims to contributed to the development of effective treatment methods by 63 examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 64 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been 65 employed to determine the most effective drug among the mentioned compound and their aquatic, 66 nonaquatic, and pharmacokinetics' data were analyzed.D rug development is a lengthy and risky work that requires significant money, resources, and labor. 68Breast and lung cancer contributes to the death of millions of people throughout the world each year, 69 according to the report of the World Health Organization, and has been a public threat worldwide, 70 although the global medical sector is developed and updated day by day. However, no proper treatment 71 has been found until now. Therefore, this research has been conducted to find a new bioactive molecule 72 to treat breast and lung cancer-such as natural myricetin and its derivatives-by using the latest and 73 most authentic computer-aided drug-design approaches. Drug-likeness, ADME, and toxicity prediction 74 were fulfilled in the investigation of Dr Akash et al, Development of new bioactive molecules to 75 treat breast and lung cancer with natural myricetin and its derivatives: A computational and 76 SAR approach, and it is noted that all the derivatives were highly soluble in a water medium, whereas 77 they were totally free from AMES toxicity, hepatotoxicity, and skin sensitization, excluding only two 78 ligands. Thus, the authors proposed that the natural myricetin derivatives could be a better inhibitor for 79 treating breast and lung cancer. 80 Lianhua
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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 source (direct Gemma or distilled Codex), 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".