Technology

We combine AI with wet lab experiments to effectively reduce the volume of experiments, shorten the R&D cycle, and cut down the costs. In the cycle between dry lab and wet lab, it enhances the precision and confidence of our AI models, thereby increasing the clinical success rate of drugs.

Generative AI —Advancing Towards De Novo Protein Design

We are the first to apply generative diffusion models to molecular design. Deep generative models are expressive approximators for high-dimensional probability distributions. We have a repertoire of expressive generative models at hand for evaluating sample quality and generating novel samples.

Example novel proteins designed by PROTSEED. (a) Extending the loop of a native protein (marked in red). (b) Novel β−barrel design with different sizes. (c) Transmembrane protein complex design with a custom number of (twelve) α−helices.

Our Advantages:

Expanded search space: Traditional rational design can optimize only a few amino acid sites at a time, while generative AI can design and optimize entire protein segments, such as the HCDR3 region.
Customized generation: Design specific proteins according to binding targets/catalytic functions/property requirements; As far as imagination reaches, we strive to realize it for you.
Rapid iteration: Quickly iterate models and molecules in high-throughput wet experiments to obtain the best molecules at the greatest speed.

Geometric Deep Learning – Understanding Proteins Based on Structure

Geometric deep learning is a cutting-edge deep learning technique that models proteins and other molecules in 3D space with the necessary symmetry in “mind”. We develop powerful GDL methods to dive deep into protein structures.

Geometric deep learning is integrated in multiple BioGeometry pipelines

Our Advantages:

Fast and accurate structure prediction: Our self-developed antibody structure prediction algorithm is 20 times faster than traditional methods with higher accuracy and smaller error.
Insight into molecular binding modes: Accurate modeling of the binding interface enables effective de novo design and directed evolution of macromolecules.
Anticipate risks and modify molecules: Based on the structure, various risks such as molecular stability and immunogenicity can be predicted. Coupled with generative AI for molecular modification, this can greatly increase the success rate of projects.