Scientific work in ChatGPT: The rigor you would give an experiment

Ask ChatGPT about a drug target and you get a fluent answer. It does not show you what it left out. Did it find the trial registered this year, the EMA decision, the patents filed around the target? In the lab, nobody accepts a result without knowing the controls and where the reagents came from. This session applies the same rigor to the records behind a scientific answer, by connecting ChatGPT to the Amass scientific data layer.

Kris starts from a new ChatGPT chat, adds Amass from ChatGPT's plugin directory, and begins with a known target. From there he follows the links across the six *Cores that Amass serves in ChatGPT, from the gene to the drugs that act on it and the records about them.

What we will cover

1. Connecting Amass in ChatGPT: finding Amass in the plugin directory and signing in once with your Amass account
2. From gene to drug: looking up a receptor in GeneCore, then the drugs that act on it in DrugCore, with mechanism and highest clinical stage
3. From drug to evidence: the trials running now in TrialCore and the published studies in BioMedCore
4. Approvals and patents: the FDA and EMA record in RegulatoryCore, and the filings around the target in PatentCore
5. Checking the output: opening the records behind the answers on ClinicalTrials.gov, PubMed and the agencies' own pages
6. Q&A: ten minutes for research questions from the audience

Who this is for

Researchers and scientists in drug discovery, clinical development and competitive intelligence, and leaders and decision makers in drug development, who already use ChatGPT and want to know what sits behind its answers.

35 minutes, followed by 10 minutes of Q&A. The session will be recorded.

Hosted by Kris Modig, Scientific Solutions and Enterprise Growth Lead at Amass.

Amass works in ChatGPT Plus, Business, Enterprise and Edu. Add it from ChatGPT's plugin directory and sign in once with your Amass account, with no API key needed: amass.tech/mcp