Computational Workflow Design
Efficient and Reliable Computational Workflows, Built for Science
Automated workflows reduce trial-and-error, save compute costs, and deliver faster, more reliable results to guide R&D decisions
Define success, constraints, and resources
Understanding objectives, scientific questions, data assets, regulatory constraints, and success metrics. Inventory current/on-prem vs. cloud resources and set decision thresholds so every downstream choice maps to a clear outcome.
Workflow integration and cloud strategies
A customized strategy integrating molecular simulations, quantum chemical calculations, and data analysis pipelines is proposed. This includes planning the use of cloud computing or high-performance computing (HPC) resources, emphasizing scalability and reproducibility.
Pipeline building and automation
A small-scale prototype of the workflow is developed, incorporating automated job scheduling and workflow orchestration, to test feasibility and identify potential challenges. This pilot study helps de-risk larger engagements.
Enhancing efficiency, reproducibility, and resource management
The workflow is optimized for efficiency, transparency, and reproducibility, integrating version control and secure data storage. Cost-effective resource management and parallel execution for large-scale simulation campaigns are planned.
Production-grade workflows and user training
The final production-grade workflow is deployed, accompanied by comprehensive documentation and necessary team training. This ensures long-term reproducibility and adaptability.
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Compare our plans
Depending on system size, compute usage & level of support.
Project Scope | 1–2 systems, low–medium complexity | Multiple systems, high complexity | Long-term support, flexible tasks |
Methods | Basic/diverse docking; short–mid MD; µs-level MD; preliminary ML | Extended MD; FEP/TI; DFT; custom ML/workflows | Priority resources; advisory analysis; ad-hoc studies |
Deliverables | Full report + reproducible workflow (includes quick results + summary) | Complete technical dossier + reusable pipeline | Continuous deliverables with monthly milestones |
Use Cases | Feasibility, lead triage, publication-ready prep | Lead optimization, regulatory/Review-ready submissions | Ongoing R&D and parallel projects |
Customizable Report | Fixed Templates |
FAQ
Expert Insights. Scaled to Your Needs
How does the consulting process work?
Our process follows six steps: Scope Determination → Solution Proposal → Pilot Study → Result Presentation → Evaluation → Finalization & Execution. This ensures transparency and alignment at each stage.
What engagement models do you offer?
We offer fixed-price, milestone-based, time-and-materials, and retainer models, depending on project needs and level of support required.
How long does a typical project take?
Timelines depend on complexity, but small pilot studies can be completed in 2–4 weeks, while full-scale projects usually take 2–3 months or more.
Who owns the results and intellectual property?
Clients retain full ownership of results and foreground intellectual property. We work under NDA and provide clear IP agreements.
What types of systems do you work on?
We work across nucleic acids (natural and chemically modified), proteins, small molecules, polymers, and aqueous or complex chemical systems. Our workflows are adaptable to diverse research questions in biology, chemistry, and materials science.
Can you integrate experimental data into the modeling workflow?
Absolutely. Experimental observations such as binding assays, thermodynamic measurements, or structural data can be used to calibrate, benchmark, and validate our computational results.
How reliable are the predictions?
Our results are supported by validation against reference data, convergence diagnostics, and explicit reporting of uncertainties. We emphasize reproducibility and clearly state limitations alongside predictions.
Can you customize workflows for specific problems?
Yes. Every project is tailored to the client’s system, objectives, and available data. We design flexible workflows that balance accuracy, scalability, and cost.
What deliverables will I receive at the end of a project?
Deliverables typically include a detailed report with figures and tables, curated datasets, and reproducible workflows or scripts. All results are prepared to be publication- or presentation-ready.
Do you work with both academic and industry groups?
Yes. We collaborate with academic labs, biotech startups, and established companies worldwide.
Can you support grant or funding applications?
Yes. We provide preliminary computational results, methods descriptions, and figures that can strengthen the technical case of grant or funding proposals.
Can you scale computations using cloud resources if needed?
Yes. We routinely deploy workflows on cloud platforms for large-scale simulations, ensuring cost-efficiency, scalability, and secure data management.
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