Molequ.
Simulate before you synthesize.
A GPU-native engine for structure prediction, virtual screening, molecular dynamics, generative design, and predictive scoring—connected directly to the scientific reasoning loop.
Move only the best candidates into expensive wet-lab work.
Wet-lab screening can consume months and millions before a team learns that a candidate was unstable, weakly binding, or unsafe. The most valuable question is often not how to run another assay, but which molecule is worth making.
Molequ combines physics-based simulation and predictive AI so Sciento can propose, simulate, rank, and hand off a smaller set of candidates with the full evidence trail attached.
A computational discovery workbench.
Structure prediction
Predict proteins and complexes through private model endpoints and supported pipelines.
Virtual screening
Dock and rank large compound libraries against program-specific targets.
Molecular dynamics
Evaluate binding stability and conformational behavior over GPU-accelerated trajectories.
Generative chemistry
Propose novel molecules conditioned on target, property, and synthesis constraints.
Predictive scoring
Estimate affinity, ADMET, toxicity, and developability before synthesis.
Active learning
Select the next-best candidate from simulation and experimental outcomes.
From target to ranked candidate set.
Set the target
Capture biological context, constraints, and success criteria.
Build the search space
Combine libraries, known compounds, and generated candidates.
Screen and stress-test
Run structure, docking, MD, and predictive scoring workflows.
Advance the best
Send ranked candidates and evidence to Assayla or the wet lab.
Built around canonical GPU and HPC workloads.
Simulation linked to real outcomes.
Spend wet-lab time on better candidates.
Connect molecular simulation to the same system that plans, observes, and verifies the experiment.