Editorial: Hot trends in computer-aided drug design techniques
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,009 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,020 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».