Inductive's absorption, distribution, metabolism, excretion, and toxicity (ADMET) models predict the chemical and pharmacokinetic properties that determine whether a molecule can become a viable drug. By surfacing these predictions directly in your conversation, scientists can evaluate compounds, triage ideas, and prioritize which molecules to synthesize, without leaving their existing workflow.
Through this MCP server, users can access Inductive’s models for the physicochemical properties LogD (lipophilicity) and pKa (acid/base ionization), key drivers of solubility and permeability.
How to use
- 1Add it in ChatGPT
Open Inductive in ChatGPT's Plugin directory (button above), select + to add it and sign in if asked.
- 2Use it in a chat
Type @ in the composer and pick Inductive, or start your message with its name. ChatGPT may also suggest it when it fits what you ask.
Skills 3
- Compare Molecular Analogs
Compare matched predictions, deltas, and property trends
- Inductive Bio
Route molecular-property prediction and comparison workflows
- Predict Molecular Properties
Predict available properties for exact SMILES
Things to ask
- “Compare LogD across the 10-compound NSAID/analgesic panel, showing exact SMILES and ranking the returned values”
- “Compare acidic and basic pKa across captopril, enalapril, quinapril, cilazapril, ramipril, and lisinopril”