ChemAmp: Amplified Chemistry Tools via Composable Agents
Summary: arXiv:2505.21569v3 Announce Type: replace-cross
Abstract: Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool constraints. To this end, we propose tool amplification, a novel paradigm that enhances the collective capabilities of specialized tools through optimized, dynamic coordination within individual tasks.
Instantiating this paradigm, we introduce ChemAmp, a computationally lightweight framework that dynamically treats chemistry tools (e.g., UniMol2, Chemformer) as composable building-block agents. It constructs task-specialized super-agents that transcend atomic tool constraints with limited data (≤10 samples). Our evaluations across four core chemistry tasks molecular design, molecule captioning, reaction prediction, and property prediction demonstrate that ChemAmp outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. Critically, this bottom-up construction strategy enables 94% inference token cost reductions versus vanilla multi-agent systems.
Introduction
The field of chemistry has experienced significant advancements with the introduction of large language models (LLMs) and their capability to orchestrate various tools. However, these models often struggle with single-task performance, primarily due to the limitations inherent in the tools themselves. ChemAmp aims to overcome these challenges by employing a novel approach called tool amplification.
Tool Amplification Paradigm
Tool amplification is conceived as a strategy to enhance the functionality of specialized tools through optimized coordination. By treating each chemistry tool as a composable agent, ChemAmp constructs super-agents that can efficiently tackle complex tasks without being hindered by individual tool constraints.
ChemAmp Framework
ChemAmp is designed to be a lightweight computational framework that dynamically combines various chemistry tools. This innovative approach allows the framework to operate effectively even with limited data, requiring as few as 10 samples for training. The framework’s architecture includes several key components:
- Composable Agents: Each chemistry tool functions as an independent agent that can be combined with others.
- Dynamic Coordination: The framework optimizes the coordination between tools to enhance overall performance.
- Task Specialization: Super-agents are formed based on the specific tasks they are designed to perform.
Core Evaluations
We evaluated ChemAmp across four critical chemistry tasks:
- Molecular Design: Generating novel molecular structures based on specified criteria.
- Molecule Captioning: Describing molecular structures in natural language.
- Reaction Prediction: Anticipating the outcomes of chemical reactions.
- Property Prediction: Estimating the properties of molecules based on their structures.
Results from these evaluations demonstrate that ChemAmp significantly outperforms both chemistry-specialized models and generalist LLMs, showcasing its effectiveness as a versatile tool in the chemistry domain.
Conclusion
ChemAmp represents a significant step forward in the orchestration of chemistry tools, offering enhanced capabilities through its innovative tool amplification paradigm. By reducing inference token costs by 94% compared to traditional multi-agent systems, ChemAmp not only improves performance but also ensures efficiency in computational resources.
As the landscape of AI in chemistry continues to evolve, frameworks like ChemAmp may pave the way for more sophisticated and effective approaches to scientific problem-solving.