Science
The models, databases and methods behind ProteinInsight — and their limits. Every result is research-use only and labelled with its provenance.
Purpose-built for the entire drug-development journey
Target discovery to post-market — twelve stages in one coherent workspace, not generic project tooling.
Methods & models
What each analysis computes, and the confidence you should place in it.
Structure prediction
ESMFold for single sequences (public API, up to ~400 residues) and AlphaFold DB for any length by UniProt accession. Each residue carries a pLDDT confidence (0–100); low-pLDDT regions are unreliable.
Molecular docking
Poses a ligand in a target's binding pocket and scores the interaction. Scores rank hypotheses for triage — they are not binding-affinity measurements.
ADMET prediction
Rule- and descriptor-based estimates of absorption, distribution, metabolism, excretion and toxicity risk to flag liabilities early.
PK/PD simulation
Compartmental pharmacokinetic exposure and pharmacodynamic response curves under configurable dosing, for what-if exploration.
Safety signals
Adverse-event mining from openFDA FAERS and bioactivity from ChEMBL. Post-market reports are signals, not proof of causation.
Data sources
External data is fetched live from pinned public databases and cached for interpretation.
Protein sequences, function, and cross-references, resolved by accession.
Experimentally determined 3D structures.
Precomputed AlphaFold predicted structures, any length.
ESMFold single-sequence structure prediction.
Curated bioactivity and medicinal-chemistry data.
FAERS adverse-event reports for post-market safety.
ProteinInsight is a research tool. Nothing here is validated for clinical, diagnostic, or regulatory use, and predictions must be confirmed experimentally.