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OpenEye Machine Learning and Molecule Explainer

Build machine learning models, Assess generated models, Visualize and understand the results – all in one environment.

In a challenge for computer aided drug design, machine-learning techniques are typically opaque. They provide unknown-box algorithms for prediction or recommendation but do not explain those results.

Different from other machine learning methods, OpenEye lets you build models, assess quality, and understand your results in a chemically intuitive way.

OpenEye Machine Learning Molecule Explainer
With OpenEye’s Molecule Explainer, machine learning is no longer just a unknown-box system. Scientists can visualize areas of a molecule the algorithm considers important for the predicted property.

Features

  • Transparent. Explains predictions with molecular annotations
  • Confidence. Domain of Application is provided with the predicted values
  • Performance. Extremely fast model building and computation
  • Turn-key. Guided workflows and pre-trained models (solubility) are provided
  • Control. Highly parameterizable for novice use and expert control
  • Modular. Integrate with other physics-based and cheminformatics methods and pipelines
OE ML Floe Results with Class Confidence
OpenEye’s machine learning results let you easily assess the quality of the prediction. For each prediction, a class confidence (low, medium, or high) is provided to help you determine trustworthiness of the predicted value.

OpenEye’s guided workflows (Orion® Floes) are designed to help you quickly build and assess your machine learning models. The built-in Molecule Explainer feature provides you with easy to understand visual explanation of the predicted physical properties for your small molecules.

Learn More

OpenEye’s machine learning functionalities help you use data-driven decision making models, speed up your drug-design process, and save cost by reducing failure rates.

WATCH OpenEye’s miniWebinar recording from September 8, 2022 on machine learning with one of our domain expert scientists, Sayan Mandal, Ph.D.

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