Science-Centered AI at OpenEye
At OpenEye we believe AI methods to be divisible into two classes: transparent, such as random forest and Gaussian mixtures, and opaque such as large language models and graph neural networks.
As a science-first company, our preference is always for the transparent over the opaque. We believe that an effective model is an interpretable model, and that the most valuable models are those that assist users in developing their own intuitions and insights, thereby enhancing human intelligence rather than replacing it.
AI to predict molecular properties
Currently, validated AI-driven methods for rapidly calculating 2D molecular descriptors to predict key molecular properties, such as solubility and toxicity, are already integrated into our Orion® molecular design platform.
In addition to pre-built models, Orion empowers users to develop and validate their own customized models to predict properties of relevance to them.
Learn About Model Building
3D molecular descriptions and AI
Moving beyond commonly used 2D descriptors to a more physically relevant 3D molecular description.
Using our core strength in the description and handling of molecular shape and electrostatics we provide AI with a more complete and relevant description of molecules than is used in standard methods.
Learn About Shape
AI-Driven Structure-Based Design
By harnessing the power of machine learning, we have accelerated large-scale structure-based virtual screening by up to tenfold. This innovation paves the way for rapid and efficient exploration of multi-billion molecule databases, significantly enhancing the efficiency and speed of drug development. Unlock new opportunities, discover novel therapeutic candidates, and bring life-saving treatments to patients faster than ever before.
Learn about Gigadock™ Warp
