AlphaProteo can design novel, high-strength protein binders targeting diverse molecules, including ones associated with cancer and diabetes.

True: published AlphaProteo results (Google DeepMind's September 2024 blog post and technical report, or later peer-reviewed work) show the model designing novel protein binders with high binding affinity against several different target proteins, including at least one target linked to cancer (e.g. VEGF-A, PD-L1) and at least one linked to diabetes or its complications (e.g. TrkA, IL-17A, or another target the documentation associates with diabetes), as of November 2025. Mostly true: the results show novel, high-affinity binders against diverse targets including cancer-associated ones, but the link to diabetes is indirect or loosely stated (e.g. the target is linked to a complication of diabetes rather than to diabetes itself). Mixed: novel binders were designed, but the high-strength claim or the claimed range of targets is only partly supported. Mostly false: the binders were only computationally predicted without experimental validation of their strength, or they target only a narrow set of molecules. False: AlphaProteo does not exist or has not been shown to design binders as described.

Set before the research began.

1. E1 AlphaProteo generates novel proteins for biology and health research Google DeepMind · 5 September 2024 Own record

   “AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A, which is associated with cancer and complications from diabetes.”

2. E2 De novo design of high-affinity protein binders with AlphaProteo arXiv (authors from Google DeepMind) Own record

   “We identified 9 total binders with sub-nanomolar \(\mathrm{K_{D}}\) values: 4 for IL-7RA, 2 for PD-L1, 1 for TrkA, and 2 for VEGF-A (Table S7).”

3. E3 De novo design of high-affinity protein binders with AlphaProteo alphaXiv · 12 September 2024

   “Only eight targets were tested experimentally, all with a target crystal structure as input, and SC2RBD, PD-L1, and TrkA were used during method development, so their rates may overstate performance on new targets.”

4. E4 AlphaProteo generates novel proteins for biology and health research Google DeepMind · 5 September 2024 Own record

   “However, we know that our AI system has limitations, as it was unable to design successful binders against an 8th target, TNFɑ, a protein associated with autoimmune diseases like rheumatoid arthritis.”

E2 reports measured sub-nanomolar KD binders for several targets including VEGF-A and PD-L1 (both cancer-associated), supporting novel high-strength binders against diverse targets. E1 names VEGF-A as linked to cancer and to complications of diabetes, which is an indirect diabetes link rather than a direct one. Under the criteria this indirect diabetes connection fits mostly_true. E3 and E4 note limits (few targets tested, one failure) but do not overturn the core result.