Albion TechBio builds dynamics-trained neural networks for medicinal chemistry. We rank congeneric series in minutes, so chemists know which compounds to make next, before the synthesis cycle starts.
Drug binding is a dynamic process. Most computational scoring still treats it as a still image, learning from frozen crystal structures. Albion trains on molecular dynamics, so its rankings reflect the physics of binding rather than a memorised snapshot. That is what makes the model hold up on targets it has never seen.
The Albion engine learns from molecular dynamics trajectories, the same simulations medicinal chemists already trust to ground-truth their FEP+ runs. The result is an architecture that scores in seconds on commodity CPU and stays accurate when a target has never been seen before.
Most scoring models are trained on labelled binding affinities. Labelled data is sparse, biased toward well-studied targets, and gives the model no information about why a ligand binds, only how strongly it bound in one experiment.
Albion takes the opposite path. We run molecular dynamics on the protein and ligand together, then train the network to recover physically meaningful targets from the trajectory. Stage 1 sees zero affinity labels. The model learns what physically stable binding looks like, then ranks unseen ligands by how close they get to it.
Inference runs in under 1.5 seconds per compound on a laptop CPU. A 500-compound congeneric series is ranked in minutes. The output is a per-compound priority with a confidence interval, so the team can hand the top 30 to FEP+ or directly to synthesis.
Boltz-2, IsoDDE, and Schrödinger's FEP+ all need a GPU cluster. Albion drops into the existing chemistry pipeline as a CPU API. That is what makes it the integration partner, not the procurement battle.
200 to 500 compound congeneric series. SMILES with docked or cofolded poses. Standard medicinal chemistry inputs, nothing exotic.
16-checkpoint ensemble. ~1.5s per compound on commodity CPU. No GPU. Zero affinity labels in stage-1 training.
Per-compound priority with confidence intervals. Hand the top 30 to FEP+ or to synthesis. The chemist stays in the loop.
On the FEP+ congeneric benchmark and on three held-out novel targets, Albion is competitive with or beats methods trained on 15 to 469 times more labelled data. On the targets where static methods sit at chance, Albion ranks first.
Weighted-mean Pearson PCC across 199 compounds and 8 targets, 95% CI [0.393, 0.594]. Second only to AEV-PLIG augmented (0.59), which trains on roughly 98,000 labelled crystal complexes. Albion uses none.
On three held-out novel targets, Albion ranks first against every supervised baseline tested. Where static methods sit at chance because they have never seen the target before, dynamics-trained scoring continues to discriminate.
Same GNN backbone, two heads. One ranks compounds for synthesis priority. The other catches the cases where cofolding tools confidently produce physically implausible poses. Both run on CPU.
A founder who has run drug-discovery operations and built the model from first principles, advised by a senior medicinal chemist with 25-plus years of pharma experience.
We are working with chemistry teams running one to four lead programmes. Pilots take a single congeneric series, run it through Albion, and produce a ranked list inside a week. If the answer is good, we go from there. If it is not, you have lost a week.