MD-Agentic
AI-Driven Multi-Target Discovery for Multi-Specific Biologics
Founded: April 2025
Stage: Seed
Company Overview
MD-Agentic is pioneering an open-source, agent-centric framework that accelerates the discovery of novel multi-target hypotheses for multi-specific biologics. Our platform leverages the Model Context Protocol (MCP) to create secure tool invocations across a distributed network of specialized AI agents that collaborate to identify promising target combinations.
The company's flagship repository, mcp-labs, provides a comprehensive reference implementation that marries Pydantic-AI, Motia workflow steps, and the Model Context Protocol for enterprise-grade biomedical discovery workflows. Our technology enables pharmaceutical researchers to rapidly evaluate target combinations that would be impossible to assess through traditional methods.
Based on cutting-edge research in multi-agent systems and biomedical AI, MD-Agentic is positioned to transform early-stage drug discovery by making multi-specific biologics development more efficient, targeted, and cost-effective.
Technology & IP
Our core technology consists of three integrated components:
MCP Catalog & Tool Registry
A shared catalog system that allows any agent to call any compliant tool without requiring bespoke wrappers, enabling unprecedented flexibility in agent-to-agent interactions.
Biomedical Agent Engineering IDE
A specialized development environment offering semantic code search, inline documentation generation, and one-click step deployment, optimized for biomedical agent engineering.
Motia Flow Orchestration
Production-ready workflow engine that encodes scientific functions like knowledge retrieval, target scoring, and synergy prediction as testable, observable steps.
Intellectual Property:
Our IP strategy combines open-source core technology with proprietary extensions. The base MCP integration layer is open-source to encourage adoption, while our specialized biomedical workflows, target assessment algorithms, and domain-specific agent optimizations are protected as trade secrets and through strategic patents.
Market Opportunity
The global biologics market is projected to reach $509.23 billion by 2027, growing at a CAGR of 7.0%. Multi-specific biologics represent one of the fastest-growing segments within this market, with particular applications in oncology, autoimmune diseases, and inflammatory conditions.
Key Market Drivers:
- Rising demand for targeted therapies with improved efficacy and reduced side effects
- Growing interest in biologics that can address multiple disease pathways simultaneously
- Increasing investment in AI-driven drug discovery ($4.8B global investment in 2024)
- Pharmaceutical industry shift toward collaborative, data-driven R&D models
Target Customers:
Our platform targets mid to large pharmaceutical companies, biotechnology firms specializing in biologics development, and academic research institutions focused on multi-target drug discovery. Early adopters include specialized biologics research teams seeking to accelerate their target discovery pipelines.
Team
Leadership:
Dr. Amir Talias, CEO & Co-founder
PhD in Computational Biology (Stanford), previously led AI drug discovery at Genentech. 15+ publications in computational biology and machine learning.Moshe Beeri, CTO & Co-founder
MS in Computer Science (Technion), former CTO at BioCompute Systems. Expert in distributed systems and AI frameworks.We're Hiring: Python Expert
Looking for a Python Star to join our team.
Team Size:
12 full-time employees: 3 executives, 6 engineers/ML specialists, 2 computational biologists, 1 operations
Milestones & Roadmap
Achieved:
- MVP development of core platform (April 2025)
- First open-source release of mcp-labs repository
- Initial collaboration with AION Labs
Next 12-18 Months:
- Q3 2025: Complete two pharmaceutical pilot projects
- Q4 2025: Release specialized IDE for biomedical agent engineering
- Q1 2026: Validate platform with successful target discovery for oncology application
- Q3 2026: Series A funding