Pred-hERG 4.0

a machine learning app to assess the cardiac
toxicity via hERG inhibition

Pred-hERG 4.0

Support the decision making in early stages of
Drug Discovery!

stay tuned! More features are coming soon!

Powerful

Pred-hERG is based on statistically significant and externally predictive QSAR models of hERG blockage. The models were built using the largest publicly available dataset of structurally diverse compounds including variety of drug classes.

Dataset

The largest publicly available dataset for hERG liability was retrieved from the ChEMBL 21 database containing 16,932 associated bioactivity records for the hERG K+ channel.

Machine learning Technology

Developed as tool for indentify putative hERG blockers. The Consensus models were generated averaging the predictions of individual models, achieving balanced accuracy, sensitivity, and specificity up to 0.89-0.90 with the coverage of 0.63-1.

Similarity Maps

This method allows visualizing how a fragment can contribute to the activity of the compound (positively or negatively).

Fast as light

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Flexible Layouts

We’re POLO, a creative agency located in the heart of New York city. Suspendisse consectetur fringilla luctus. Fusce id mi diam, non ornare orci. Pellentesque ipsum erat, facilisis ut venenatis eu.

Predict a single molecule

Instructions

Insert SMILES

Directly paste the SMILES representation of the desired chemical structure.

or Draw

Draw the structure using the "Molecular Editor".

or Load a file

Click the right button on the whiteboard of the "Molecular Editor" and select "Paste MOL or SDF or SMILES"." SDF and MOL files are accepted.

Predict

Click on the “Predict hERG Liability” button.

Draw molecule or load a file

Get in Touch with Us

Our Headquarter is in Brazil

Headquarters:
LabMol, Faculdade de Farmácia, Universidade Federal de Goiás
Rua 240, Qd. 87, Setor Leste Universitário, Goiânia, Goiás 74605-170, Brazil.
Phone: (+55) 62 3209 6451
Fax: (+55) 62 33209 6037
Email: carolina@ufg.br
Feature Request:
Use this form to request new features or suggest modifications to existing ones
Bug Report:
Your comments, suggestions, and ideas for improvements are very important to us.

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