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We design and run computational chemistry workflows that turn complex molecular questions into clear, quantitative answers. Our focus is on free energy perturbation (FEP), ΔΔG predictions, molecular dynamics simulations, and mechanistic modeling for biotech, pharma, and ag-biotech teams.
Whether you’re optimizing a lead series, mapping drug-resistance mutations, or redesigning enzymes, we help you make decisions based on physics-based, reproducible calculations—not guesswork.
We build and execute FEP pipelines to predict binding free energies and relative ΔΔG values across ligands or mutations.
What we can help you with
Comparing binding affinities across a ligand series
Ranking design ideas before synthesis
Evaluating the effect of point mutations on binding
Prioritizing which candidates move forward to experiments
What you get
Well-documented FEP setup and protocol description
Plots and tables of predicted ΔG / ΔΔG values with uncertainties
Discussion of convergence, limitations, and confidence level
A concise, decision-focused report summarizing key takeaways
We are engine-agnostic and can adapt to your preferred MD environment, while keeping the workflow transparent and reproducible.
Drug resistance often emerges through point mutations that reshape the free energy landscape. We specialize in ΔΔG predictions for mutations to help you understand and anticipate resistance.
Typical questions we address
Which mutations are likely to weaken drug binding?
How do different clinical variants compare in terms of ΔΔG?
Can we prioritize second-generation designs against resistant mutants?
Deliverables
Per-mutation ΔΔG predictions for selected residues
Ranked lists of mutations (stabilizing, neutral, destabilizing for binding)
Visualizations of mutation sites on the structure
Strategic commentary to support medicinal chemistry decisions
Contact Us
Have a target, mutation set, or enzyme in mind—and a timeline to match?
Tell us what you’re working on, and we’ll help you see where FEP, ΔΔG predictions, and time-resolved MD can make the most impact.
We’re a remote-first team based between Türkiye and Germany, and we typically work with biotech, pharma, and ag-biotech groups who want clear, decision-ready computational results, not just raw data.
What to include in your message
To help us scope things quickly, it’s useful (but not mandatory) if you share:
A short description of your target / system
The type of questions you want answered (e.g. ranking ligands, mutation ΔΔG, enzyme variants)
Any timing constraints or key milestones
Your preferred way of working (one-off study, pilot, ongoing support)