Drug discovery. Done by the model.
Most discovery platforms automate one step — docking, virtual screening, assay scheduling — and hand the rest off to email. Aether closes the whole loop. The model proposes the design; the autonomous-lab discipline runs the plate; the readout updates the model.
What the model actually computes.
These are the workloads the AI runs in drug discovery — rendered live in SVG. Each panel is the geometry the model is reasoning about, the physics it solves for, and the validation it lands against measured reality.
Aether is the AI doing the drug discovery work.
Three things to know about the AI underneath every workload on this page. We aren't building a chat surface for your team — we are training a foundation model that plans, runs and reports.
The AI does the discipline
Not a chat interface over your engineers. A foundation model that plans the study, picks the method, runs the agents, validates against measured reality and writes the report. The same shape an experienced practitioner would follow — done by the model.
One model, your context
We don't ship a sibling model per industry. The same Aether weights serve every discipline, but the agents on top are trained against your standards, your historical data and your regulatory posture.
Reality is the regulariser
We train on simulation, instrument and design data — not scraped text. The model gets better the more reality it touches. Reality is what tells it when it's wrong.
Lyrebird Therapeutics.
“It's the first platform we've used where the readout actually goes back into the model.”
Closed the discovery loop on a fibrosis programme.
The work, done by Aether.
These are the workloads Aether already runs in drug discovery. Not a wish-list. Each ships with validated benchmarks and traces a senior engineer can read end-to-end.
- 01
Generative chemistry
Goal-conditioned molecule generation, scaffold hopping, lead optimisation with synthetic-accessibility constraints, multi-parameter optimisation across potency, selectivity and ADMET.
- 02
Docking and FEP
Pose generation, scoring, induced-fit refinement, alchemical free-energy perturbation at scale. Coupled to MD where convergence demands it.
- 03
ADMET
All major endpoints — solubility, permeability, hERG, CYP inhibition, hepatotoxicity, clearance, half-life — calibrated to your assays, not a public split.
- 04
Biologics
Antibody and binder design, paratope prediction, developability scoring, immunogenicity flags, ADC linker chemistry.
- 05
Autonomous wet-lab
Active-learning campaigns running on liquid handlers, acoustic dispensers and protocol-export robotics — with eBR-grade audit and chain-of-custody.
- 06
Translational PBPK
Physiologically-based pharmacokinetic modelling, exposure-response, first-in-human dose prediction.
“We are building the AI that does drug discovery — not the one that talks about it. Aether doesn't ask your engineers to drive its solvers. It runs the work, validates the artefact and writes the memo.”
The stack today.
The typical stack in drug discovery — a half-dozen seven-figure renewals stitched together with internal glue code. We replace the spine; we don't touch the niches that work.
The agents in the mix.
Aether coordinates these specialist agents for drug discovery. Each is independently deployable, but the coordination — and the foundation model underneath — is what makes them more than a folder of scripts.
How we ship into regulated drug discovery.
The pieces that matter to procurement, security review and the regulator. We ship into industries where the boundary is real and the audit is unforgiving.
GxP-grade chain-of-custody from compound to candidate
21 CFR Part 11 audit-ready electronic records
FDA AI/ML guidance-aligned model lifecycle management
Biosecurity refusals and dual-use gating at the model layer
Other companies shipping with us.
A slice of the customers we're allowed to name. The pattern repeats: one workload, one pilot, one comparison memo we co-author.
- Aerospace
Northwind Aerospace
Retired a $1.8M CAE renewal across two quarters.
62%wall-clock reductionRead the story - Chip design
Helix Silicon
RTL to signoff on a 4nm SoC — without an incumbent-EDA renewal.
0EDA license serversRead the story - Software
Fern Platform Co.
One coding agent across every IDE surfaces.
15vendor adapters in productionRead the story
The AI is also running here.
A foundation model that serves twenty-two industries today. The same weights, different agents, different pilot patterns. Browse the ones nearest yours.
Questions about drug discovery.
What does Aether replace in drug discovery?
The incumbent stack here — Computational-chemistry suites, Docking + FEP tools, Molecular-dynamics packages, Pharmacophore software and more — collapses into one model and one project file, with Aether for drug discovery and Aether for autonomous labs doing the work under one safety story.
What outcomes should we expect?
9 weeks (hypothesis → measured hit); Endless (discovery workloads · one model); GxP (audit-grade chain-of-custody) — scoped to your workload and measured against your own acceptance criteria.
How does a pilot work?
Three to eight weeks against a workload and a win condition you co-author. We run alongside your incumbent stack on real cases; if we aren't better on the metric by quarter end, the rest of the quarter is on us.
Can it deploy inside our boundary?
Yes — managed, in your VPC, on-prem or fully air-gapped on Aether Cloud, with the audit trail and provenance the regulated parts of this work require.
The AI is ready for drug discovery.
Send us the workload that hurts. We'll come back with a scoped pilot — three to eight weeks, win condition co-authored. If we aren't better on the metric by quarter end, the rest of the quarter is on us.