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.
- Transparent. Explains predictions with molecular annotations
- Confidence. Domain of applicability 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
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.
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.
And, you can also WATCH OpenEye’s miniWebinar recording from September 8, 2022 on machine learning with one of our domain expert scientists, Sayan Mandal, Ph.D.