Science can already tell you what a molecule is and what it looks like. What it has never been able to tell you, cheaply and at scale, is how the thing behaves once it meets the messy conditions of the real world. That gap is where drugs quietly fail in trials, where food products miss the palate they were built for, and where, increasingly, artificial intelligence runs out of road.
Apoha, a London company spun out of 15 years of interfacial physics, says it has built the missing measurement. On 3 June it emerged from stealth with $36M in funding, announced at the Frontier Technologies Stage at SXSW London.
The round is led by Singular, with participation from Tim Draper’s Draper Associates and continued backing from seed investors Redalpine, Seedcamp, Wilbe and Nucleus, alongside grant funding from Innovate UK.
The company calls its data layer Liquid State Intelligence, a new category it places alongside sequence and structure. Where genomics digitised the language of biology and structural biology digitised design, Apoha wants to digitise behaviour: what matter actually does under stress. The funding, it says, will go toward making that a foundational data class for biologics, food, materials and physical-world AI.
The science traces to 2008, when founder and chief executive Shamit Shrivastava began working on a problem the Nobel-winning Hodgkin-Huxley model of nerve signalling had left open: the physics of the boundary where matter meets liquid.
He went on to publish evidence for two-dimensional solitary sound waves at a lipid interface in 2014, work the company says was later named among Scientific American’s discoveries that could change everything. In 2021 he co-founded Apoha with Anshika Srivastava, its chief operating officer and a former executive director at Goldman Sachs.
The company now holds more than 60 patents across hardware, software, data and AI models.
Its first product is VIBE, an empirical readout of how a sample behaves under controlled stress. The platform takes a quantity of material small enough to sit on a pinhead, suspends it in liquid, applies a sequence of perturbations, and records the wave patterns the molecule throws off in response.
Those patterns resolve into more than 1,000 measured descriptors of behaviour in a single reading, where conventional assays capture one property at a time. Within minutes, the company says, a VIBE readout can flag whether an experimental drug will fail before it reaches a trial.
The platform is already in commercial use, and the firmest evidence sits in a preprint. In joint research with Boehringer Ingelheim, a multi-year commercial partner, Apoha identified high-risk antibody candidates with greater than 90% precision from as little as 8 micrograms of material.
A second version of the benchmarking work reports the platform outperforming 12 industry-standard developability tests across 236 clinical antibodies, and surfacing information the conventional measures miss rather than duplicating them.
Other customers point to range. Apoha is working with German biotech Ethris on predicting how lipid nanoparticles carrying mRNA behave in animals, and with plant-based food company THIS on a protein replacement bound for supermarket shelves. It also lists Somru BioSciences and several Fortune 500 companies across pharma, food and materials.
The wider bet is that physical-world AI will eventually need this. Models have learned to see and read, and a generation of physical AI systems is now being built to act on matter. None of them can yet feel how a drug dissolves or how a flavour holds, because that data has never been collected at scale.
“It cannot be scraped from the internet, synthesised, or retrofitted from existing assays,” Shrivastava said. “It has to be measured.” Whether enough buyers agree to make a data class out of it is the question the next round will have to answer.
