TL;DR
ByteDance’s drug discovery unit Anew Labs presented its first AI-designed therapy at a major immunology conference in Boston, showing a generative-AI-designed small molecule targeting IL-17, a protein-protein interaction long considered undruggable.
The unit also published AnewOmni, a generative framework trained on 5 million biomolecular complexes that claims to be the first to design functional molecules across all scales. ByteDance has entered the AI drug discovery race alongside Isomorphic Labs, Anthropic, and Insilico Medicine.
The company that built TikTok’s recommendation algorithm, the system that predicts with unsettling accuracy what a person wants to watch next, is now using a related class of AI to predict how molecules will behave inside a human body. 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 spent decades calling undruggable because the binding surfaces are too large and too flat for conventional small molecules to disrupt. Anew Labs says its AI found a way in.
The presentation in Boston was the first time ByteDance showed the world what its drug unit has built. It will not be the last. The company is registered to exhibit at the BIO International Convention in San Diego in June, and its head of computational chemistry will present at the Free Energy Workshop in Barcelona next week.
The unit
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Anew Labs operates from Shanghai, Singapore, and San Jose, California, with 36 core team members listed on its website and a scientific advisory board that 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 advisory board reads like a recruitment list from the companies that dominate biologics and immunology, disciplines where the targets Anew Labs is pursuing have historically required injectable antibody therapies costing tens of thousands of dollars per year. The unit’s ambition is to replace those injections with oral pills, using generative AI to design small molecules that can do what antibodies do but in a form that 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 by treating psoriasis and other inflammatory conditions. An oral small molecule that achieves comparable efficacy would be commercially transformative, both cheaper to manufacture and easier for patients to take.
The challenge is that IL-17’s binding surface with its receptor is a protein-protein interaction, a broad, shallow interface that gives small molecules very little to grip. The gap between what AI can do in a laboratory and what it delivers to patients remains the defining tension of health technology, and IL-17 is precisely the kind of target where that gap is widest.
The model
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 in the world, and PCSK9, a cholesterol-related protein, achieving success rates between 23 and 75 per cent 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 is significant because it 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. A model that designs small molecules cannot typically also design peptides or protein-based therapeutics.
AnewOmni claims to be the first framework to succeed at functional molecular design across all scales, which, if validated in clinical settings, would give Anew Labs a platform capability rather than a single-programme capability. Isomorphic Labs, the DeepMind spinoff backed by Eli Lilly and Novartis, released its own drug design tool in February that doubles the accuracy of AlphaFold 3, and has partnership agreements with combined milestone values of up to $3 billion. The race to build the definitive AI drug design platform is global, and ByteDance has entered it with a model that, on paper, addresses a limitation that its competitors have not yet publicly solved.
The context
ByteDance is not the first technology company to move into drug discovery. Anthropic acquired Coefficient Bio for $400 million in an acqui-hire that brought fewer than ten people into the AI company’s biological research efforts. Google’s DeepMind has been working on protein structure prediction since AlphaFold’s breakthrough, which won the 2024 Nobel Prize in Chemistry. Microsoft has invested in biology-focused AI through its partnership with Paige, a computational pathology company.
Nvidia has built BioNeMo, a platform for training and deploying biomolecular AI models. The pattern is consistent: the companies with the most advanced AI infrastructure are redirecting a portion of that capability toward biology, because drug discovery is a problem shaped like the problems AI is good at, searching vast combinatorial spaces for rare solutions that satisfy multiple constraints simultaneously.
What distinguishes ByteDance’s entry is the source of its AI expertise. TikTok’s recommendation engine is, at its core, a system that models human behaviour by processing enormous quantities of data and predicting which combinations of content will produce the desired response.
Anew Labs’ generative models do something structurally similar: they process enormous quantities of molecular data and predict which combinations of atoms will produce the desired biological response. The mathematical architectures are not identical, but the organisational capability, the ability to train large models on massive datasets, iterate rapidly, and deploy at scale, is transferable. ByteDance’s AI infrastructure, built to serve 1.5 billion TikTok users, is now being applied to a problem where the users are molecules and the engagement metric is binding affinity.
The test
More than 173 AI-discovered drug programmes are now in clinical development globally, with 15 to 20 entering large-scale trials this year. Whether AI will revolutionise drug development depends on how it is used, and the industry’s 90 per cent clinical failure rate has not yet demonstrably improved.
Insilico Medicine’s rentosertib, a first-in-class drug for idiopathic pulmonary fibrosis where both the target and the molecule were discovered using AI, showed positive Phase IIa results published in Nature Medicine. The Recursion-Exscientia merger created the most comprehensive AI drug discovery platform in the industry, but then discontinued its lead AI-discovered candidate after long-term data did not confirm earlier efficacy trends. The pattern across the field is promising early data followed by the same biological reality that has always made drug development difficult: molecules that work in a dish do not always work in a body.
Anew Labs has four pipeline candidates and a generative platform that, if its preprint results hold, can design functional molecules across scales. It has the backing of a parent company valued at roughly $300 billion with AI infrastructure that dwarfs most pharmaceutical companies’ computational resources. It has advisors from Innovent, Amgen, and Takeda.
What it does not yet have is clinical data. The IL-17 molecule presented in Boston was preclinical. The distance from a poster at an immunology conference to an approved oral therapy that replaces injectable antibodies is measured in years and billions of dollars, and most molecules that start that journey do not finish it. The most ambitious AI-biology startups are the ones whose founders understand that the algorithm is the beginning, not the end.ByteDance built an algorithm that changed how a billion people consume content. Whether the same company can build an algorithm that changes how a disease is treated is a question that no conference presentation can answer. Only a clinical trial can.
