ByteDance’s drug discovery unit, Anew Labs, presented its first AI-designed therapy at the American Association of Immunologists’ annual meeting in Boston in mid-April, showing data on a small molecule designed by generative AI to inhibit IL-17, a cytokine involved in autoimmune diseases including psoriasis, rheumatoid arthritis, and ankylosing spondylitis. The molecule targets a protein-protein interaction, a category of drug target that the pharmaceutical industry has long considered undruggable because the binding surfaces are too large and too flat for conventional small molecules to disrupt.
What Anew Labs does
Anew Labs operates from Shanghai, Singapore, and San Jose, California, with 36 core team members listed on its website. Its scientific advisory board includes Liu Yongjun, former president of Innovent Biologics; Ji Ma, a former principal scientist at Amgen; and Hua Zou, scientific director of protein chemistry at Takeda California. The unit’s ambition is to replace injectable antibody therapies—which cost tens of thousands of dollars per year—with oral pills, using generative AI to design small molecules that can do what antibodies do but in a form patients can swallow.
Chris Li, head of biology, presented one of Anew Labs’ four pipeline drug candidates in Boston. The molecule is a pan-spectrum IL-17 inhibitor, meaning it is designed to block multiple forms of the IL-17 cytokine rather than a single variant. Existing IL-17 therapies, including Novartis’s secukinumab and Eli Lilly’s ixekizumab, are injectable antibodies that generated billions in annual revenue. An oral small molecule that achieves comparable efficacy would be cheaper to manufacture and easier for patients to take.
The AI model: AnewOmni
In March, Anew Labs published a preprint on bioRxiv describing AnewOmni, a generative AI framework trained on more than five million biomolecular complexes. The model is designed to work across molecular scales—from small chemical compounds to peptides to nanobodies—assembling chemically meaningful building blocks at atomic resolution. In the preprint, the researchers demonstrated that AnewOmni could design functional molecules targeting KRAS G12D, one of the most studied oncology targets, and PCSK9, a cholesterol-related protein, achieving success rates between 23 and 75 percent with only low-throughput laboratory validation.
The model uses programmable graph prompts that allow researchers to steer the generation process by specifying chemical, geometric, and topological constraints. The technical approach attempts to solve a problem that has limited AI drug discovery across the industry: most generative models work well at one molecular scale but fail when asked to design across scales. AnewOmni claims to be the first framework to succeed at functional molecular