AI Autopilot for Computational Biology on AWS
Fovus benchmarks every structure prediction and molecular dynamics run, then auto-assigns the GPU and Spot strategy that gets you from sequence to structure without babysitting infrastructure.
The challenges
Structure prediction and molecular dynamics live and die on GPU selection — the wrong CPU/GPU pairing, memory configuration, or Spot strategy is the difference between a $0.10 structure prediction and one that costs ten times more, or a simulation running at a few hundred ns/day instead of thousands. GPUs are also the most contested resource in the cloud: availability shifts by region and hour, provisioning queues stall research, and a configuration hand-tuned for one protein system needs its own tuning pass for the next one with a different atom count and bottleneck profile. Most teams end up either overpaying for a safe, oversized GPU, or under-provisioning and watching a run stall.
How we help
Fovus auto-benchmarks every structure prediction and molecular dynamics workload — from Boltz-1 and AlphaFold 3 to GROMACS and OpenMM — against representative inputs, free of charge, revealing exactly how GPU choice, CPU pairing, and memory configuration affect runtime and cost for that specific biomolecular system.
Based on that benchmark data, Fovus auto-assigns the GPU and CPU combination proven fastest or cheapest for each run — the tight CPU-GPU coordination structure prediction and MD both depend on, tuned per workload instead of guessed once.
Minimize cost, minimize runtime, or balance both — the same benchmark data lets Fovus assign a completely different GPU strategy depending on what a given run needs most, without you re-tuning anything by hand.
Fovus applies checkpointing and Spot-to-Spot failover so long-running MD trajectories can run on Spot pricing without losing hours of simulation to an interruption.
As AWS rolls out new GPU generations, Fovus re-benchmarks and upgrades your strategy automatically — no manual re-tuning required when a faster or cheaper instance type becomes available.
Deploy via a single CLI command, a few clicks in the web UI, or the Fovus Python API. No cluster to provision, no environment to set up — pay only for the runtime you use.
The numbers behind the Autopilot
What computational biology teams see running structure prediction and MD on Fovus and AWS
67%
Lower cost for large-batch protein folding with AlphaFold 3, using a two-stage AI-optimized HPC strategy.
1,139 ns/day
Fastest molecular dynamics throughput benchmarked with GROMACS on a speed-optimized run.
Business Impact
Teams running structure prediction and molecular dynamics on Fovus get real, benchmarked numbers instead of estimates: Boltz-1 structure predictions for as little as $0.10, AlphaFold 3 protein folding at up to 67% lower cost, and molecular dynamics with GROMACS or OpenMM running as fast as 1,139 ns/day or as cheap as $6.59/µs, depending on what a given run needs most.
Your next structure prediction could be next.
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