DeepMind's AlphaProtein Novo designs new-to-nature enzymes — a preprint, not a proof

Google DeepMind posted a preprint on 5 October describing AlphaProtein Novo, a machine-learning pipeline that designs enzymes from scratch instead of mutating ones biology already made. The team reports two enzymes with no natural counterpart: a nitrene transferase that assembles piperidine, a ring that appears in a large share of approved drugs, and a serine esterase that breaks down DEHP, a plasticiser regulators have linked to reproductive harm. The preprint sits on bioRxiv, has not been peer-reviewed, and every performance figure in it is the authors' own measurement.

A diffusion model pointed at reaction chemistry

AlphaProtein Novo, or AP Novo, starts with a catalytic motif — a small arrangement of residues that performs the chemistry — and a ligand it must act on. A diffusion model co-generates a protein structure and sequence around that motif, and the candidates are filtered using AlphaFold 3 predictions of mechanistically relevant atomic details, then scored by evaluating ensembles of sequences derived from the same backbone. The pipeline is a generative model for enzymes, in the same family as the binders AlphaProteo produced a year ago. DeepMind published the code, though the pretrained weights sit behind a separate terms-of-use download and the repository carries its own output-use policy. The announcement also linked a second preprint and supporting materials, without naming the second paper.

DeepMind tested the system on five reactions. Three are model reactions used to develop the method; two are the practical targets. Every row below is a company measurement, taken in the company's own assays.

Reaction	Lead design	Reported result
Piperidine synthesis	GDM_NT_270	22 turnovers, 99:1 regioselectivity, 94% e.e.
DEHP hydrolysis	GDM_DEHP_376	47 turnovers at 90 °C, against 3.3 at room temperature
Kemp elimination	GDM_KE_1872	kcat/Km of 44,940 per M per s at pH 10
Serine ester hydrolysis	GDM_SE_2937	kcat/Km of 360,000 per M per s
Styrene cyclopropanation	GDM_CT_103	above 99:1 diastereomeric ratio, 96% e.e.

The piperidine result is real; the framing around it is not

The piperidine synthase is the cleaner demonstration. Chemists can build the six-membered ring, but getting an enzyme to favour it over the five-membered pyrrolidine is hard: in a screen of 188 natural and previously engineered heme proteins, DeepMind found nine above its activity threshold and none that preferred piperidine by more than 30:70. Its designed lead inverted that bias, reaching 22 turnovers and a 99:1 regioselectivity ratio. The company calls this an improvement of two orders of magnitude in total turnover over a wild-type protoglobin, a natural protein. Some secondary coverage has flattened it into a claim that the enzyme makes piperidine roughly 99 times faster than the industrial method manufacturers use. That comparator is not in the preprint, and the 99 in the paper is a ratio, not a speed-up factor.

The DEHP enzyme trades activity for toughness

The DEHPase is a different kind of claim. At room temperature the designs were less active than natural DEHP-degrading esterases — the best reached 6% of one natural enzyme's productivity after four hours. Where they win is durability. GDM_DEHP_376 is 14-fold more active at 90 °C than at room temperature and 2-fold more active in 75% acetonitrile, conditions under which both natural esterases tested, including a thermophile-derived one, were completely inactive. That matters because DEHP is nearly insoluble in water, and the cosolvents that dissolve it tend to inactivate natural enzymes. The preprint is explicit that the enzyme only partly degrades the molecule, to MEHP, and that full breakdown to phthalic acid is still needed before any real-world cleanup.

The authors' own caveat is the sharpest line

A sentence in the discussion is worth more than the headlines. The researchers write that their models "do not meaningfully model the physics of catalysis," and add that de-novo catalytic activities remain orders of magnitude below natural enzymes or directed-evolution variants. Constructing the motifs still requires significant expertise, and generating enough samples for one motif can be "practically prohibitive," they say. This is a preprint: no peer review, and no third-party replication of any result. The pattern echoes Anthropic's enzyme-system discovery, another vendor-led biology claim that independent labs have yet to reproduce.

The reception ran ahead of the evidence

Pushmeet Kohli, DeepMind's science lead, announced the work on X, framing it as evidence that the system can design proteins that "go beyond what is known in nature"; the post drew roughly 800 likes. The busiest community thread, on r/singularity, ran to roughly 580 points and about 40 comments under the title "DeepMind's new AI designed enzymes from scratch" — a gauge of interest rather than evidence. On a quiet day in this field, that is the whole argument: a working pipeline and a public repository are more than most AI-for-biology announcements deliver, but a preprint is not proof. The work sits beside the recent AI-scientist result on graphene metamaterials, another case of a model proposing a physical artifact its authors have not yet fully characterised.

What would settle it

The next steps are unglamorous. An independent lab synthesising the published sequences and measuring the same reactions would move the piperidine and DEHP claims from reported to reproduced. Peer review would test the assay thresholds, which are the authors' own. And the DEHPase would need to finish the job it starts — full hydrolysis to phthalic acid under industrial conditions — before it leaves the bench. DeepMind's own framing is the one the evidence supports: de-novo design has become a complement to natural enzyme diversity, not a replacement for it.