A new theory for drug design: conformational selection
Drug design based on the conformational selection theory is more accurate in reflecting the nature of macromolecular targets and how they are regulated.
AI technology combined with molecular
simulation for better efficiency and precision
Addressing protein engineering needs
across multiple scenarios
A new theory for drug design:Drug design: from a structure-based approach to a conformation-based approach
Drug design based on the conformational selection theory is more accurate in reflecting the nature of macromolecular targets and how they are regulated.
We calculate the conformational changes of targets/drugs and their physicochemical interactions using today’s powerful high-performance computers.
Our AI platform allows us to design new drugs based on dynamic conformations obtained from big data, to refine predictions through molecular simulation, and to better understand the dynamics of atoms, molecules, functional domains and their associations.
The selection of the dynamic conformations of receptors and ligands
Dual allosteric effect of biologics
Designing and predicting drugs quickly and accurately using AI
The efficient design and regulation of conformational selection for polypeptides
The design and optimization of therapeutic antibodies
Drug design: from a structure-based approach to a conformation-based approach.
We calculate the conformational changes of targets/drugs and their physicochemical interactions using today’s powerful high-performance computers.
Our design approach has been calibrated and validated by wet lab experiments. We use AI to predict the properties of molecules such as their druggability and affinity.
Our founder Dr. Buyong MA is a renowned scientist in computational biology. He proposed the conformational selection theory. He is a leader in studying antigens and antibody structures for neurodegenerative diseases.