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Integrating CADD and Herbal Informatics Approach to Explore Potential Drug Candidates Against HPV E6 Associated With Cervical Cancer
Published in Shazia Rashid, Ankur Saxena, Sabia Rashid, Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
Arushi Verma, Jyoti Bala, Navkiran Kaur, Anupama Avasthi
Ligand preparation: Identification and analysis done thorough literature search through NCBI indicated Luteolin [15] and Daphnoretin [16] as anti-neoplastic, antiviral agents and apoptosis inducing. Physiochemical analysis were done using PubChem [17] and 3-D structures of both the hits were built and visualized. ChEMBL [18] and Drugbank [19] were used to gather relevant information about the ligands like its sources, alternative forms, activity charts, clinical data and so forth.
Introduction
Published in Anton C. de Groot, Monographs in Contact Allergy, 2021
A general description of the index drug with indications, mechanism of action and sometimes additional relevant data are provided here. Important sources were ChemIDPlus, PubChem, DrugBank (https://go.drugbank.com/), Drug Central (https://drugcentral.org/), Drugs.com (https://www.drugs.com/), and Wikipedia (https://www.wikipedia. org/).
Translational personalized medicine: Molecular profiling, druggable targets, and clinical genomic medicine
Published in Priya Hays, Advancing Healthcare Through Personalized Medicine, 2017
“The Druggable Genome Is Now Googleable” read a November 2013 headline of Bio-IT World, a website devoted to next-generation technologies and personalized medicine. The article delves into a searchable database that would obviate the need for a bioinformatician by the pharmacogenomics researcher. Obi and Malachi Griffith developed the idea of the Drug Gene Interaction Database (DGIdb), a free, searchable online database of drug–gene associations. The Griffith brothers claim that this database can be used by the non-informatics expert. Entering search terms brings up a chart of drug–gene interactions that are culled together from public databases such as DrugBank, the Therapeutic Target Database, and PharmGKB. It was a labor-intensive activity to set up the DGIdb, but the result is a Google-like website that can reveal drug–gene interactions through search filters (Krol, 2013).
Systems pharmacology approach to explore the mechanisms of shufeng Jiedu Capsule on treating H1N1 infection
Published in Drug Development and Industrial Pharmacy, 2023
Zhoufang Mei, Jing Zhang, Xuru Chen, Yanchao He, Jingjing Feng, Yong Du, Jindong Shi, Zhijun Jie
We used a proprietary system for predicting potential drug targets based on two powerful approaches, random forests (RF) and support vector machines (SVM), which effectively integrate chemical, genomic, and pharmacological information. The datasets used to build these models include drug molecules and protein molecules that interact with known compounds from the Drugbank database. To obtain the experimental dataset, a numerical vector of drug-target pairs was constructed by concatenating chemical descriptors and protein descriptors. An RF is a classifier that contains multiple decision trees, and its output classes are determined by the way the individual trees infer classes. RF uses the bootstrap resampling method to extract multiple samples from the original sample, model each bootstrap sample as a decision tree, then combine the predictions of multiple decision trees, and obtain the final prediction result by voting. The performance of the models was evaluated with an internal five-fold cross-validation and four external independent validation methods.
Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features
Published in Expert Opinion on Drug Discovery, 2022
We downloaded SMILES notations of each drug/compound in our dataset from the DrugBank and ChEMBL databases, via string matching between drug names obtained from ClinicalTrials.gov and names provided in drug/compound entries in DrugBank and ChEMBL. In order to construct the dataset of regulatorily approved drugs, we searched for drug names in ‘phase IV’ clinical trial records in the ClinicalTrials.gov database. During this search, we ignored trials in which drug combinations were utilized. In order to avoid including auxiliary small ions, compounds with SMILES notations up to the length of six characters were removed from the dataset, as well. We also added approved drug information from the DrugBank database for the corresponding indications on top of the ones extracted from the ClinicalTrials.gov database. In the end, we obtained 14 datasets, one for each disease group, containing approved drug-indication pairs.
Proteomics based drug repositioning applied to improve in vitro fertilization implantation: an artificial intelligence model
Published in Systems Biology in Reproductive Medicine, 2021
Roberto Matorras, Raquel Valls, Mikel Azkargorta, Jorge Burgos, Aintzane Rabanal, Felix Elortza, Jose Manuel Mas, Teresa Sardon
Market access: Only candidates annotated as ‘approved’ in Drugbank (Wishart et al. 2008) were selected. Nutraceutical compounds were also considered. Safety: The information about safety issues was collected from SIDER database (Kuhn et al. 2016). Candidates causing severe adverse drug reactions in > 1% of patients or linked to reproductive failure were discarded (see Supplemental Table S1). Administration feasibility: substances whose administration is hardly feasible were discarded, such as foreskin keratinocytes and fibroblasts. FDA Pregnancy Category (Boothby and Doering 2001): Drugs labeled as X (Boothby and Doering 2001) (meaning that adequate studies in animals or pregnant women have demonstrated evidence of fetal abnormalities or risks) were also discarded. Using this filtering protocol, the list of 1202 compounds with a predictive value > 70% for either EI effectors or the identified key proteins reduced to 23 drugs (Figure 3).