دورية أكاديمية
أعرض في EDS

Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy.

التفاصيل البيبلوغرافية
العنوان: Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy.
المؤلفون: Alshehri FF; Department of Medical Laboratories, College of Applied Medical Sciences, Ad Dawadimi 17464, Shaqra University, Saudi Arabia.
المصدر: Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society [Saudi Pharm J] 2023 Dec; Vol. 31 (12), pp. 101835. Date of Electronic Publication: 2023 Oct 20.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Saudi Pharmaceutical Society Country of Publication: Saudi Arabia NLM ID: 9705695 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1319-0164 (Print) Linking ISSN: 13190164 NLM ISO Abbreviation: Saudi Pharm J Subsets: PubMed not MEDLINE
أسماء مطبوعة: Publication: Riyadh : Saudi Pharmaceutical Society
Original Publication: Riyadh, Kingdom of Saudi Arabia : The Society,
مستخلص: Epilepsy, a prevalent chronic disorder of the central nervous system, is typified by recurrent seizures. Present treatments predominantly offer symptomatic relief by managing seizures, yet fall short of influencing epileptogenesis. This study endeavored to identify novel phytochemicals with potential therapeutic efficacy against S100B, an influential protein in epileptogenesis, through an innovative application of machine learning-enabled virtual screening. Our study incorporated the use of multiple machine learning algorithms, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), and Random Forest (RF). These algorithms were employed not only for virtual screening but also for essential feature extraction and selection, enhancing our ability to distinguish between active and inactive compounds. Among the tested machine learning algorithms, the RF model outshone the rest, delivering an impressive 93.43 % accuracy on both training and test datasets. This robust RF model was leveraged to sift through the library of 9,000 phytochemicals, culminating in the identification of 180 potential inhibitors of S100B. These 180 active compounds were than docked with the active site of S100B proteins. The results of our study highlighted that the 6-(3,12-dihydroxy-4,10,13-trimethyl-7,11-dioxo-2,3,4,5,6,12,14,15,16,17-decahydro-1H cyclopenta[a] phenanthren -17-yl)-2-methyl-3-methylideneheptanoic acid, rhinacanthin K, thiobinupharidine, scopadulcic acid, and maslinic acid form significant interactions within the binding pocket of S100B, resulting in stable complexes. This underscores their potential role as S100B antagonists, thereby presenting novel therapeutic possibilities for epilepsy management. To sum up, this study's deployment of machine learning in conjunction with virtual screening not only has the potential to unearth new epilepsy therapeutics but also underscores the transformative potential of these advanced computational techniques in streamlining and enhancing drug discovery processes.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 The Author.)
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فهرسة مساهمة: Keywords: Epilepsy; Machine learning; Molecular docking; Phytochemicals; S100B
تواريخ الأحداث: Date Created: 20231115 Latest Revision: 20231116
رمز التحديث: 20231116
مُعرف محوري في PubMed: PMC10641561
DOI: 10.1016/j.jsps.2023.101835
PMID: 37965486
قاعدة البيانات: MEDLINE
الوصف
تدمد:1319-0164
DOI:10.1016/j.jsps.2023.101835