PregPred: Predicting Developmental Toxicity Potential

This is an online web portal to predict developmental toxicity, described in PAPER LINK. To use, enter SMILES in the box below, or draw a compound and hit load SMILES, then click "Get Properties". Results will appear below. By default all models for all endpoints will be run. You can choose to turn off certain endpoints in the options sidebar. Fragment contribution maps are generated with RDKit. To turn on the maps, check the "Display contribution maps" in the options sidebar. It defaults to off because the maps will increase the runtime significantly, so if using please be patient. More information about these maps can be found here.

For the applicability domain calculation (AD), an ensemble confidence approach is used such that if the average prediction confidence of the ensemble of models is above 0.6, the prediction is considered "inside" the AD

Please cite [PAPER REF GOES HERE]. Models and code for this webserver can be found here.


You can also generate a CSV of the results by entering compounds below, seperated by commas or new lines. fragment contribution maps will not be generated for CSV. Large numbers of SMILES and models wil take a long time to process, be patient. It is recommended that is number of SMILES * number of model is > 100 to instead use the local standalone calculator instead of this webserver.

When uploading a SMILES file make sure smiles are newline seperated (ei a csv with one column and each row is a SMILES). Uploaded files will auto-populate the text area, wait to generate csv until you see them in the box.

Sorry, csv generation will not work on safari, use chrome, edge or firefox
You have to enable JavaScript in your browser to use JSME!

Model Options

Enabled Models

Developed by James Wellnitz and Ricardo Tieghi of the MML @ UNC

Supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM135122. The content is solely the responsibility of the authors and does not necessarily represent the official views of National Institutes of Health

© 2024, all rights reserved