Leading the charge in AI+
Pharmaceutical & biology industry
Generative AI, deep learning and
high-throughput biopharmaceutical
Generating and designing antibody sequences at massive scale, predicting their properties, validating via experiments, and self-improving iteratively. We balance the tradeoff between exploration and exploitation and search the sequence landscape, aiming for more precise epitope targeting and better developability after each iterative design cycle
BDS3*
The huge dataset BDLD3* we built contains a variety of research data. Among them, antigen-antibody binding data, protein drug and polysaccharide binding data, protein drug and small molecule binding data.
BDL3*
We train deep learning models to predict binding between antibodies-antigens, protein drug and polysaccharide binding data, protein drug and small molecule binding data. With structural and functional data, our models become more precise.
BML5*
We use machine learning technology to extract important features in huge data, including antigen and antibody binding characteristics, binding target characteristics, protein and small molecule binding characteristics, antigen-antibody binding products, and screen and summarize all feature data.
BGAI3*
We use BGAI3* technology to generate and predict protein structures. We will extract the corresponding data and suitability from the feature database and calculate the protein sequence and spatial structure that can achieve our desired results. This step has high requirements for the accuracy of data, the quality of annotated data, and the algorithm model.
BOA1*
By repeating the previous steps, we learn from the successes and failures of earlier experiments, train better models, design a new library of candidates, and further improve antibodies with desired properties.