What you can do with MIP
Research intelligence
Ask natural-language questions across biology. Genes, variants, pathways, protein function, disease mechanisms, drugs, literature, and datasets can be explored in one conversation with source-backed answers.
Omics analysis
Bring transcriptomics, proteomics, metabolomics, single-cell, or custom tabular data. MIP can inspect files, reason over results, generate hypotheses, and write analysis code when built-in tools are not enough.
Gene and variant evidence review
Query ClinVar, gnomAD, OMIM, PubMed, dbSNP, and other resources for variant and gene evidence. Use citations to verify source records and compare evidence across databases.
Drug target and pathway exploration
Query ChEMBL, Open Targets, KEGG, Reactome, UniProt, AlphaFold, and PDB. Identify targets, map pathways, explore protein structure, and surface relevant compounds in a single conversation.
Literature synthesis
Search and reason across PubMed and preprint sources. Ask MIP to summarize a field, identify contradictions, extract key findings, or surface recent work for a hypothesis.
Code execution
Write and run Python or R directly from chat. Scientific libraries include pandas, NumPy, Biopython, scanpy, pydeseq2, matplotlib, and other analysis packages.
Autonomous pipelines
Kick off long-running computational tasks such as RNA-seq workflows, molecular simulations, file processing, or multi-step analyses. Jobs are tracked and output files are saved automatically.
Artifacts and exports
Generate structured outputs from any analysis: literature briefs, evidence tables, figures, spreadsheets, code files, paper drafts, pathway maps, or custom formats ready for review.
Explore the docs
Quickstart
Sign in, start a research chat, choose a mode, and attach files.
Platform overview
How MIP’s reasoning, data, compute, and output layers work.
Key concepts
Research modes, projects, files, tool calls, jobs, and artifacts explained.
Chat guide
How to use chat, preferences, citations, files, and artifacts.
Code execution
Running Python and R, available libraries, and compute limits.