Q: What is PhID:
A: PhID is developed to visualize network pharmacology of targets, diseases, genes, side-effects, pathways, and drugs basis of PhID, which integreted from 14 public resources. Currently, there are 306082 activities (small chemicals + biotech), 22385 target (human proteins + drug target), 10408 diseases, 43415 human genes, 4492 side-effects, 776 pathways and more than 171000 interaction information among them.
Q: Functions provided in PhID?
A: PhID addressed applications of interactive visual network pharmacology tools to network
<1> diseases, <2> targets, <3> drugs, <4> genes, <5> side-effects, and <6> pathways in a dynamics web-based network graph. also, it provided two durg-taget predict models
Q: How to use PhID?
A:The main page has seven tabs. In the first six tabs, text search is available for identifiers and common names of corresponding bio-entities. The last tab is being used as predict for drug-target interactions.
In PhID, users can search pharmacy related entities and interactions using names, ids, molecular structures, molecular fragments for different purposes. Specifically, Four notes for inquiring pharmacy related entities (drug, target, disease, gene, side-effect, and pathway) should be paid attention to:
(1) For the convenience of the user to understand PhID at a glance, there is only one searching box shared by entities query panel. The user is asked to first choose which type of the entities will be queried by click the corresponding selecting tab.
(2) Because of the lack of controlled and standardized vocabulary describing chemicals, PhID provide chemical component searches by chemical structure using a SMILES string in drug searching. And users can search drugs with a specific molecular fragment. After the SMILES string of the molecular fragment is input, drug molecules will be scanned to identify if any molecule contains the specific fragment.
(3) For any specify type of the query, using entities names or the public database identifier in searching box is workable. We recommend standard names used for search. The query will be traversed in specify entities table. If the query string is not equal to this type of entity name or id records exactly, PhID will provide alternative entities whose names or ids contain the query string. Other constraints can be applied using the identifier filter box below the searching box for the drug, disease, and pathway inquiry.
(4) In our predict models, drug features are extracted from drug SMILES format, and target features are obtained from target protein sequence in FASTA format. So, drug-target predicts in PhID using a SMILES string and a FASTA sequence.
(5)Additional, We provide tools for converting molecules to SIMILES.
Q: Searching options provided in PhID?
A: Searching can be work (1) using names,public database ids, molecular structures, molecular fragments for Drugs.
(2) using names, uniprot ids for Targets.
(3) using names, ICD10 or OMIM ids for Diseases.
(4) using names and GeneAlts ids for Genes.
(5) using names Sider ids for Side-effects.
(6) using names SMPDB ids for Pathways.
Q: Searching cases available in PhID?
Searching case: This case study is to illustrate the data integration: a drug-centric network searched by drug name ‘bromfenac’, in which red triangles, green circles, orange rectangles, darkblue hexagon, skyblue rhombus, purple clouds shaped icon correspond to diseases, drugs, targets, genes, pathways, and side-effects, respectively. The query string ‘bromfenac’ will obtain two targets, three diseases, two genes, one pathway, and fourteen side-effects. TTD id: DAP000732, ChEBI id: ChEBI: 240107, DrugBank id: DB00963, and HMDB id: HMDB15098 are integrated into drug ids, while they are shared the same SMILES ‘OC(=O)Cc1cccc(c1N)C(=O)c1ccc(cc1)Br’. The drug chemical information and medicinal description are from Drugbank and ChEBI, respectively. The drug-target, drug-disease, drug-gene, drug-side-effect, and drug-pathway relations are from Drugbank, TTD, Sider, and SMPDB. According to the following bio-entities list or the click of the graph node, results are fully traceable and referenced to the original databases.
Drug repositionning: This case study is a repositioning example to introduce usage of prediction model. Dopamine, one of the major transmitter in the extrapyramidal system of the brain, was used to treat cardiovascular and kidney diseases31. In the past research, dopamine antagonists may initiate and promote breast cancer or colon cancer. VEGF-A is a kind of growth factor active in angiogenesis, vasculogenesis and endothelial cell growth, which is an attractive cancer target of metastatic colorectal carcinoma. With the aid of PreDPI-Ki models, we found they have an off-target links by inputting the Dopamine SMILES and VEGF-A FASTA sequence in PreDPI-Ki input box of predict_model tab. This prediction was supported by the literature result, in which dopamine has been reported as a safe VEGF-A inhibitor inducing dangiogenesis and vasculogenesis in experimental tumors.