Use ADMET-AI

Official Neurosnap webserver for accessing ADMET-AI online.

ADMET Prediction Toxicity Prediction Drug Design

Overview

ADMET-AI is a machine learning model designed to predict Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of chemical compounds. Utilizing a graph neural network architecture, Chemprop-RDKit, trained on 41 datasets from the Therapeutics Data Commons, ADMET-AI offers rapid and accurate evaluations of large-scale chemical libraries. It provides a user-friendly web interface for batch predictions, facilitating efficient drug discovery processes.

Neurosnap Overview

The ADMET-AI online webserver allows anybody with a Neurosnap account to run and access ADMET-AI, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.

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Features

  • Employs Chemprop-RDKit, a graph neural network architecture, for precise ADMET property predictions.
  • Trained on 41 ADMET datasets from the Therapeutics Data Commons, ensuring comprehensive coverage.
  • Outperforms existing ADMET prediction tools in both speed and accuracy.
  • Provides contextualized predictions by comparing results with a reference set of approved drugs.

Statistics

Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.

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API Request

Access ADMET-AI using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.

Job Note

Provide a name or description for your job to help you organize and track its results. This input is solely for organizational purposes and does not impact the outcome of the job.

Configuration & Options

Service Inputs

Input molecules for ADMET property prediction. Small peptides are also supported but must contain fewer than 500 atoms in total. All inputs are converted to SMILES format, with SMILES strings being the preferred input for optimal results. Invalid molecules are automatically skipped.

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