Biopharma Sandbox Analysis for Custom Models, Statistics, ML and Mini Apps
Use natural language to run custom Python, statistics, forecasting, machine learning, optimization, visualization, ETL and mini-app analyses inside an isolated biopharma workspace.
Decision questions
What this solution is built to answer.
What analysis does this decision require if no pre-built template fits?
Can ARiDA write and run the model, chart, workbook or mini app from a natural-language brief?
Which statistical, ML, forecasting or optimization method is appropriate for the dataset and decision?
Can a one-off sandbox analysis become a reusable workflow for future data refreshes?
Capabilities
What ARiDA can run for this use case.
Custom Python for pandas, SciPy, scikit-learn, statsmodels, lifelines, PyMC, networkx and visualization libraries.
Forecasting, risk scoring, survival analysis, Bayesian modeling, market analysis and optimization across assets, trials, portfolios, grants and development plans.
Ad-hoc ETL, fuzzy matching, data validation, chart generation, Excel workbook creation, HTML reports and lightweight decision apps.
Isolated execution with model artifacts, plots, source files and audit trail returned to the workspace.
Reusable workflows from successful analyses so the same method can rerun with new inputs.
Sandbox examples
Sandbox examples
ML, statistics, optimisation — written and run on demand.
Beyond the full catalogue of pre-built chart types, ARiDA can write custom Python for any analysis you can describe. The agent codes the model, runs it in an isolated sandbox with pandas, scipy, scikit-learn, statsmodels, lifelines, PyMC, networkx, and brand-styled matplotlib + seaborn, and returns the chart, the model file, and a full audit trail back into your workspace.
Time-series & causal forecasts
Custom forecasts of peak sales, enrolment, funding flow.
- Bass diffusion peak-sales curves
- ARIMA / Prophet enrolment trajectories
- Vector-autoregression on grant flow
Risk & success scoring
Models trained on the literature, validated on holdouts.
- Phase III success predictors
- KOL ranking + sponsor footprint
- Target druggability scores
Time-to-event analysis
KM, Cox, parametric — clinical-trial-grade.
- KM with stratification + log-rank
- Cox PH with subgroup forest
- Weibull / log-normal duration sim
Probabilistic modelling
Posteriors that update as evidence lands.
- Beta-binomial PoS updates
- Hierarchical NMA across trials
- Adaptive design simulation
LP, MILP, search
Portfolio, enrolment, deal terms — quantified.
- Portfolio MILP under budget caps
- Trial-site selection optimisation
- Deal-structure parameter search
Anything you can describe
pandas, scipy, scikit-learn, statsmodels, PyMC, lifelines, networkx — on demand.
- Fuzzy-match protocol amendments
- Custom Pareto / trade-off plots
- Bespoke ETL across raw exports
Workflow table
Named workflows and expected artifacts.
| Workflow | Role | Artifacts |
|---|---|---|
| custom-sandbox-analysis | Natural-language custom model or analysis | Notebook-style code, chart, model output, source files, audit trail |
| biomedical-data-analysis | Tables, files, statistics and ML over user data | Cleaned data, figures, validation notes, repeatable method |
| portfolio-optimization | Custom optimization under budget, risk or timing constraints | Efficient frontier, allocation model, scenario outputs |
Evidence inputs
Data sources, tools, and user context.
Outputs
What the workflow should leave behind.
Deliverables
Custom model artifact with assumptions and code trace.
Charts, tables, Excel workbooks, dashboards or lightweight mini apps.
Method memo explaining inputs, transformations, uncertainty and caveats.
Reusable workflow definition for reruns with new data.
Proof points
The sandbox writes and runs custom code rather than forcing predefined dashboard metrics.
Outputs include the chart, model and audit trail needed for review.
Successful sandbox runs can be saved as workflows and rerun against refreshed inputs.
FAQ
Common evaluation questions.
Is the sandbox limited to pre-built templates?
No. ARiDA can write custom code for the question at hand, run it in an isolated workspace, and return the chart, model file, supporting data and audit trail.
Can a custom sandbox analysis become repeatable?
Yes. Analyses that prove useful can be converted into reusable workflows and rerun with new data, assumptions or cohorts.
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Related reading
How ARiDA Combines Live Web, Biomedical Databases, and Code Execution
Biotech research needs current signal, domain-native evidence, and computation in the same loop. Remove one layer and the output gets weaker.
What an AI Research System Needs to Be Useful in Pharma
Pharma teams need software that fits the realities of evidence, review, and organizational decision-making.
Designing AI Research Systems for Biotech: Tools, Workflows, and Durable State
Biotech research systems need workflow structure, specialist lanes, files, and repeatable execution paths around the model.