🤖 AI Summary
To address the challenges of poor interoperability between machine learning (ML) models and scientific computing models, as well as system instability under high concurrency in drug discovery, this paper proposes a model management platform integrating large language model (LLM) agents and generative AI. Methodologically, we design a model gateway built upon MLOps principles and microservice architecture, supporting dynamic consensus-based modeling, model registration/retrieval, asynchronous execution, and result callbacks—orchestrated autonomously by an LLM agent for intelligent model governance. Our key contribution is the first introduction of a dynamic consensus mechanism into collaborative model scheduling, augmented by generative AI to enhance meta-information understanding and operational decision-making. Experimental evaluation demonstrates zero failure rate under sustained load from over 10,000 concurrent clients, significantly improving scheduling intelligence and system scalability. The platform establishes foundational infrastructure for AI-driven drug development.
📝 Abstract
This paper presents the Model Gateway, a management platform for managing machine learning (ML) and scientific computational models in the drug discovery pipeline. The platform supports Large Language Model (LLM) Agents and Generative AI-based tools to perform ML model management tasks in our Machine Learning operations (MLOps) pipelines, such as the dynamic consensus model, a model that aggregates several scientific computational models, registration and management, retrieving model information, asynchronous submission/execution of models, and receiving results once the model complete executions. The platform includes a Model Owner Control Panel, Platform Admin Tools, and Model Gateway API service for interacting with the platform and tracking model execution. The platform achieves a 0% failure rate when testing scaling beyond 10k simultaneous application clients consume models. The Model Gateway is a fundamental part of our model-driven drug discovery pipeline. It has the potential to significantly accelerate the development of new drugs with the maturity of our MLOps infrastructure and the integration of LLM Agents and Generative AI tools.