Amgen developed a generative biology workflow using AI and machine learning that begins with a set of specifications a candidate must satisfy. Next, generative AI models suggest new designs, and predictive models evaluate and rank these designs. This is done iteratively until molecules are found that satisfy the specifications, which include criteria relevant to efficacy, safety, and manufacturability. Evaluating as many designs in silico with these generative models reduces the burden on wet labs.
“To develop models that can help us generate good biologics, we needed our platform to support rapid pretraining and fine-tuning across a range of experiments,” says Langmead. “We needed the flexibility to experiment with different data and scale. Using NVIDIA BioNeMo on DGX Cloud, we were able to easily perform distributed training of complex models in a multi-GPU environment. The capabilities and performance of NVIDIA BioNeMo and DGX Cloud were precisely what we needed and available to us when we needed them.”
“One of the key advantages of DGX Cloud was the remarkably swift onboarding process. We were able to progress from our initial login to pretraining large models in just a few days. BioNeMo on DGX Cloud is a turnkey solution—our users only need to supply data and specify the model by adjusting a few configuration files, and BioNeMo handles all other aspects of the process.”
Amgen trained protein LLM ESM-1nv in BioNeMo on DGX Cloud with Amgen proprietary antibodies. This resulted in five trained antibody-specific LLMs. BioNeMo has state-of-the art biomolecular large language and diffusion models for training and inferencing in early-stage drug discovery workflows. This includes models for generating proteins and small molecules, understanding protein and small molecule properties, predicting binding structures of small molecules bound to proteins, and predicting the 3D structure of proteins.