PapersFlow for Life Sciences & Biology Research
Streamline biology research with UniProt protein lookup (250M+ entries), PDB protein structures, deep research reports, and a Python sandbox for statistical analysis — powered by Semantic Scholar and OpenAlex.
Search millions of biology papers via Semantic Scholar and OpenAlex, look up proteins in UniProt, retrieve PDB structures, and analyze experimental data — all in one AI research assistant.
Life sciences research requires integrating information from papers, protein databases, pathway maps, and experimental datasets — each living in a different tool. You search PubMed for CRISPR delivery methods, switch to UniProt for protein data, open a separate tool for pathway visualization, and run stats in R or Python. Context is lost at every transition, and synthesizing across these sources takes far longer than it should.
What You Can Do
- UniProt Protein Lookup
- PDB Protein Structures
- Deep Research for Biology
- mRNA Sequence Analysis
Tools
Compare
Frequently Asked Questions
- Does PapersFlow integrate with biological databases beyond UniProt?
- Yes. PapersFlow integrates directly with UniProt (250M+ proteins) and PDB (protein structures with ligand data). KEGG and Gene Ontology are not directly integrated, but the AI can extract and summarize relevant information from papers that reference these resources.
- Can it handle the volume of papers in biology?
- Yes. PapersFlow searches over 474 million papers, which includes comprehensive coverage of life sciences journals, preprint servers like bioRxiv, and interdisciplinary venues. The semantic search ensures you find relevant work even across subfields that use different terminology.
- Does it support APA citation format?
- Yes. Life sciences outputs default to APA 7th edition formatting. You can export reference lists, in-text citations, and full .bib files formatted for APA. Other styles are available if your target journal requires a different format.
- Can I analyze my own experimental data alongside the literature?
- Yes. The Python sandbox supports uploading CSV or Excel files. You can run statistical analyses on your own data (t-tests, ANOVA, regression), generate figures, and compare your results against published findings — all within the same research session.