Our team is experienced in developing generative algorithms and has contributed influential papers in both generative flow networks and conditional flow matching. We also recently released FoldFlow — a series of generative algorithms that extend the flow-matching paradigm to SE(3) equivariant motions.
We actively partner with labs utilizing techniques in synthetic biology to design and profile millions of completely new proteins. From this data, we learn structural insights to help further new generations of models.
We work closely with AnyScale, Nvidia, AWS, and Google to run our computation across 1000 GPUs.
Countless lab Scientists' time and efforts are spent on molecules that will eventually fail in the later stages of development. Our approach is in combining machine learning algorithms with a feedback loop from several scalable representative biological assays to learn patterns in the data. Thus derisking the drug discovery process and empowering scientists to make informed decisions. We are building toward a comprehensive process that combines simple binding assays, functional cell assays, and single-cell patient-derived samples to account for tumor and patient heterogeneity, and finally, precisely select which group of patients should be included in the trial based on molecular data like binding and microfluidics.