Help - How to use pdCSM-GPCR
Main page
About pdCSM-GPCR
pdCSM-GPCR is a machine-learning platform to predict GPCR ligands, which relies
on distance/pharmacophore patterns encoded as graph-based and auxiliary signatures.
The platform is composed of 36 regression models, trained and tested on different experimental
data sets encompassing a diverse and complementary set of GPCRs as follows:
- Class A: P08173, P08908, P08912, P0DMS8, P20309, P21452, P21917, P24530, P28335, P29275, P30542, P30968, P34995, P35346, P35348, P35372, P46663, P47900, P48039, P50406, P51677, Q8TDS4, Q8TDU6, Q96LB2, Q99500, Q99705, Q9H228, Q9HC97, Q9Y5N1, Q9Y5Y4;
- Class B1: P47871, Q16602;
- Class C: P41180, Q14416, Q14833;
- Class F: Q99835.
Submission page
How to run a prediction
To run a prediction:
- Click on "Prediction" (1) to open the submission page.
- Provide either an input file with a list of molecules (2) in SMILES format (up to a maximum of 100 molecules) or supply a single SMILES string (3). Users are advised to use Canonical SMILES. Syntax non-compliant molecules will be ignored.
- The next step is to choose the type of prediction (4). Users can choose between running All receptors at once or running class A, class B1, class C and class F receptors separately, by clicking on its corresponding button..
Results page
Results
For a single molecule prediction, your results can be displayed as follows:
- A depiction of the uploaded molecule will be shown in (1). Make sure the depiction is what you are expecting for your molecule.
- A list of molecule properties will be calculated and shown in (2).
- The prediction will be displayed in a tabular format as presented in (3). The information shown is the Receptor ID, Name of the receptor and the predicted bioactivity in µMolar.
Contact page
Getting in touch
In case you experience any trouble using pdCSM-GPCR or have any suggestions or comments, please do not hesitate in contacting us either via e-mail or through the online form.