An AI agent that executes experiments, not just ideas.
SilicoLabs interprets your scientific intent, chooses the fitting computational method, runs the simulation across HPC resources, and delivers clear, interpretable results.
From scientific intent to running simulation
State the test you have in mind. SilicoLabs manages the path from hypothesis to result.
After you submit a hypothesis, the SilicoLabs agent interprets its scientific intent. It determines the relevant biological or chemical system, connects the question with the suitable computational method, whether molecular dynamics, protein-ligand docking, DFT geometry optimization, or ADMET prediction, then builds the complete simulation protocol without asking you to define force fields, timesteps, or convergence criteria.
The protocol enters distributed compute, where convergence and stability are tracked before structured output is returned. Your team can be away from the lab for the full cycle.
The agent maps intent to simulation parameters
SilicoLabs covers core computational chemistry and biology methods most research teams need.
Molecular motion
Model atomic movement over time to examine conformational change, binding stability, and dynamics of proteins and small molecules.
Fields: AMBER, CHARMM, OPLS. Times: 10 ns to 1 us. Readouts: RMSD, RMSF, MM-PBSA binding energy.
Protein docking
Estimate binding poses and interaction energies for small-molecule candidates against protein targets at high throughput.
Engines: AutoDock Vina, Glide SP. Readouts: binding free energy, interaction fingerprints, pose clustering.
DFT Geometry Search
Calculate electronic structure and refine geometry for reaction pathway prediction, transition state identification, and property calculation.
Methods: B3LYP, M06-2X. Basis: 6-31G*, cc-pVTZ. Output: thermochemistry, frontier molecular orbitals.
Your computations run overnight
Distributed HPC
Simulations execute on distributed HPC infrastructure. Compute expands with the job without manual allocation. Typical runs take hours.
Convergence checks
Throughout each run, the agent checks RMSD plateaus and energy convergence. Diverging jobs are flagged and re-parameterized automatically before results reach you.
Team-specific environments
Every research group's simulation data runs in its own compute environment. Input structures and results stay isolated and are not used for model training.
Results prepared before your morning meeting
Structured data, plain-language summary, and raw files. No post-processing needed.
The fluorine substituent at C4 increases binding affinity by approximately 1.2 kcal/mol compared to the baseline compound. The added halogen forms a favorable C-F interaction with a hydrophobic pocket residue at position 214. Binding pose is stable throughout the 100 ns production run.