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AlphaFold 3

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

Inference-time projection for physically valid AlphaFold 3–style biomolecular diffusion models (arXiv:2610.07037v1)

The paper proposes treating physical validity as a constrained inference problem and introduces two closed-form projection operators—an inter-chain van der Waals projection and a ligand distance-geometry projection—applied to the diffusion model's denoised coordinate estimate (x̂0). The operators are local, sparse, displacement-capped, require no network evaluations or gradients, and — when applied to Boltz-2 and OpenFold-3 across five benchmarks (CASP15, CASP16, PoseBusters monomer and complex sets, and the Boltz physical-validity set) — recover perfect physical validity while preserving structural-accuracy and ligand-placement metrics with negligible runtime and memory overhead.

7.0

RESEARCH · 1 SOURCE · Google DeepMind

SynthID Bio: watermarking AI-designed proteins while fine‑tuning AlphaFold 3

The authors introduce SynthID Bio, a family of watermarking methods that embeds imperceptible signatures into protein sequences and predicted 3D coordinates so the watermark is verifiable on synthesized proteins; in wet‑lab tests on three targets (VEGF‑A, SARS‑CoV‑2 RBD, PD‑L1) watermarked protein binders retained hit rates, binding affinities, and sequence diversity comparable to unwatermarked designs. For folding, the approach fine‑tunes part of AlphaFold 3’s diffusion network to make predicted coordinates carry a detectable signature while preserving prediction accuracy and structural feature distributions.

8.0