MADD: Multi-Agent Drug Discovery Orchestra
career value
210K/year
🤖 AI Summary
In early-stage drug discovery, hit identification remains costly, and AI tools exhibit high usability barriers for wet-lab researchers. This work introduces the first multi-agent collaborative system tailored for drug discovery, which automatically translates natural-language instructions into end-to-end molecular generation and virtual screening workflows by integrating large language models (LLMs) with domain-specific molecular modeling tools—including molecular docking, generative modeling, and property-based filtering. Key contributions include: (1) AI-first de novo design against five novel therapeutic targets; (2) construction of the first benchmark dataset comprising over 3 million query–molecule pairs; and (3) statistically significant performance gains across seven evaluation tasks versus state-of-the-art LLM-based approaches, culminating in the discovery and public release of multiple validated hit candidates. The system substantially enhances accessibility and practical utility of AI-driven drug discovery for experimental biologists and medicinal chemists.
Application Category
📝 Abstract
Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer the application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.
Problem
Research questions and friction points this paper is trying to address.
Addresses hit identification challenges in early drug discovery
Improves accessibility of complex AI tools for wet-lab researchers
Enhances virtual screening through multi-agent orchestrated pipelines
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-agent system orchestrates customized drug discovery pipelines
Four coordinated agents handle compound generation and screening
Natural language queries build and execute hit identification workflows
🔎 Similar Papers
💼 Related Jobs
