Expert-led infrastructure for scientific workflows.
in4r is led by Ivo Djidrovski, PhD, combining toxicology and safety-science workflow experience with practical AI infrastructure development.
Ivo Djidrovski, PhD
Built by someone who has lived inside the scientific workflow.
in4r is led by Ivo Djidrovski, a scientist and AI systems builder working at the intersection of toxicology, computational workflows, and auditable agent infrastructure. The work is practical: make AI useful where evidence, data privacy, and expert accountability matter.
Scientific foundation
PhD and postdoctoral work across stem-cell biology, toxicology, and next-generation safety assessment.
AI infrastructure builder
Creator/maintainer of public open-source tooling, including O-QT-related workflows and ToxMCP modules.
Published and citable
O-QT publication, ECETOC AI trust work, public VHP4Safety-related outputs, and ToxMCP preprint/suite development.
Community validation
ONTOX and MIT/Roche hackathon wins plus practical work with research consortia and safety-science teams.
Peer-Reviewed Science Behind the Tools
Our infrastructure is backed by published research in computational toxicology. Every tool we build has a scientific foundation you can cite.
ToxMCP: Auditable MCP Servers for Computational Toxicology
IN PREPBuilding trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop
O-QT assistant: A multi-agent AI system for streamlined chemical hazard assessment and read-across analysis using the OECD QSAR toolbox API
PUBLISHEDThe Virtual Human Platform for Safety Assessment (VHP4Safety) project: Next generation chemical safety assessment based on human data
Report of the First ONTOX Hackathon
PUBLISHEDStart with one case-study pilot or advisory retainer.
Bring one safety-science or research case study. We will define the pilot boundary, consulting scope, evidence sources, review gates, and deployment path before scaling anything. We are opening pilot and design-partner conversations for teams operationalizing AI in review-heavy scientific workflows.