AI Agents
Biologists need genetic evidence. Computational analysts spend months wrangling data. By the time results come back, the science has moved on. Inference Agents eliminate friction by delivering instant PhD-level answers using generative AI, letting biologists iterate rapidly and computational teams focus on science, not data processing. An "autonomous mode" executes complex workflows without human intervention, allowing you to scale your team 10x.
We've built multiple ways to access Inference Agents, because your workflow shouldn't adapt to our platform.
A purpose-built conversational UI for accessing agents with features designed for scientific rigor. See every API call the agent makes, via sources. Tables, figures, and outputs are exportable. Pick up where you left off with conversation history.
MCP means Inference works wherever you already work. Connect to Claude Desktop, ChatGPT, or any MCP-compatible client. Link to other MCP servers and knowledge bases for a fully integrated discovery experience.
Integrating directly into Gene Explorer lets you converse with the data you're viewing. Ask questions, request synthesis across thousands of associations, and get instant context on therapeutic implication, all without leaving the page.
This is where things get interesting.
Inference Agents can run fully autonomous and adaptable workflows, complex computational biology and genetics pipelines that execute without human intervention. Break up tasks, run parallel analyses, chain results together, and produce final reports.
"Validate ANGPTL3 as a target for hypercholesterolemia. Assess causality using MR, determine effect direction from QTLs, scan for safety liabilities across all phenotypes, and identify existing drugs hitting the target."
"Identify opportunities for indication expansion for TYK2 drugs. Get all drugs targeting TYK2, find the variant most associated with autoimmune disease, run a PheWAS, and compare associations with indications already being pursued."
"Identify all IBD-associated genes using genome-wide embeddings. For each gene, assess the likelihood it's causal using QTL colocalization and fine mapping, then create a therapeutic hypothesis. Highlight novel, druggable targets with strong genetic evidence."
In this example, an agent was asked to triage the top associations from a UK Biobank hypertension GWAS. It executed over 300 API calls, retrieved GWAS results, searched for functional annotations, ran on-the-fly colocalization with QTL studies, and identified the most likely causal gene at each locus, all without any user intervention.
Enabling autonomous mode is as simple as clicking a button. It uses the most advanced models with extended reasoning to carry out complex multi-step workflows like this one.
Inference Agents combine large language models with real-time access to the platform and specialized genomic drug discovery instructions. The LLM reasons, the platform provides data, and the instructions supply context, resulting in an autonomous system that plans and executes complex analyses that would take a computational biologist days.
With Inference Agents, we're scaling the productivity of our lean computational biology team to levels that exceed pharma teams ten times larger. The same agents powering our internal programs and supporting our discovery partnerships are available to Inference partners.
Experts can focus on strategy and interpretation while agents handle the computational heavy lifting. Non-experts gain instant access to analyses without specialized training. Everyone moves faster.
Agents are powered by cutting-edge AI tools that transform genomic drug discovery.
Explore AI Discovery →