[Paper] Model Gateway: Model Management Platform for Model-Driven Drug Discovery

Source: arXiv - 2512.05462v1

Overview

The Model Gateway paper introduces a purpose‑built platform that brings modern MLOps capabilities to the drug‑discovery workflow. By treating scientific simulation codes and machine‑learning models as interchangeable services, the system lets researchers register, invoke, and combine models at scale—​even leveraging LLM agents and generative‑AI tools to automate routine management tasks.

Key Contributions

• Unified Model Management Layer – A single API and control panel for registering, versioning, and monitoring heterogeneous models (e.g., quantum‑chemistry simulators, deep‑learning predictors).
• Dynamic Consensus Modeling – Built‑in support for “consensus” models that aggregate predictions from multiple scientific models on‑the‑fly, improving robustness without manual ensemble coding.
• LLM‑Driven Automation – Integration of large‑language‑model agents that can issue model‑registration commands, schedule runs, and parse results, reducing human‑in‑the‑loop overhead.
• Scalable Execution Engine – Asynchronous job submission with a proven 0 % failure rate when handling >10 k concurrent client requests.
• Role‑Based UI & Admin Tools – Separate dashboards for model owners (self‑service) and platform administrators (policy enforcement, quota management).

Methodology

The authors built the gateway as a micro‑service stack:

1. Model Registry – A metadata store (SQL/NoSQL) that tracks model artifacts, versions, required inputs, and resource footprints.
2. Gateway API – A REST/GraphQL interface exposing CRUD operations for models and a job‑submission endpoint.
3. Execution Orchestrator – A task queue (e.g., RabbitMQ or Kafka) that dispatches model runs to containerized workers (Docker/Kubernetes), handling retries and result callbacks.
4. Consensus Engine – A lightweight aggregator that pulls outputs from multiple workers, applies weighting or voting schemes, and returns a unified prediction.
5. LLM Agent Layer – Prompt‑engineered LLMs (GPT‑4‑class) that translate natural‑language requests into API calls, enabling “chat‑with‑the‑gateway” interactions.

The platform was stress‑tested using synthetic workloads that mimic real‑world drug‑discovery pipelines (e.g., virtual screening of millions of compounds). Performance metrics (latency, success rate, resource utilization) were logged and compared against a baseline ad‑hoc script‑based approach.

Results & Findings

Key takeaways: the gateway eliminates failure points caused by manual scripting, cuts onboarding time dramatically, and provides a reusable consensus layer that improves predictive reliability without extra engineering.

Practical Implications

• Accelerated Lead Identification – Researchers can spin up new predictive models (e.g., binding affinity estimators) and immediately plug them into the screening pipeline, shortening the “hit‑to‑lead” cycle.
• Reduced Ops Overhead – DevOps teams no longer need bespoke scripts for each model; the platform handles container provisioning, scaling, and logging automatically.
• AI‑Assisted Workflows – LLM agents can schedule batch runs, fetch results, and even suggest hyper‑parameter tweaks, enabling a semi‑autonomous discovery loop.
• Regulatory Traceability – Centralized metadata and versioning simplify audit trails required for FDA‑type submissions.
• Cross‑Team Collaboration – Model owners can expose their models as services, while downstream analysts consume them via a stable API, fostering reuse across chemistry, biology, and data‑science groups.

Limitations & Future Work

• Domain Specificity – The current implementation is tuned for drug‑discovery workloads; extending to other scientific domains may require custom adapters for data formats and simulation packages.
• LLM Reliability – While LLM agents automate many tasks, occasional hallucinations in generated API calls necessitate validation layers.
• Resource Cost Modeling – The paper does not quantify cloud‑cost savings; future work could integrate cost‑aware scheduling to optimize GPU/CPU usage.
• Security & Access Controls – Fine‑grained permissioning for proprietary models is mentioned but not fully explored; a hardened authentication/authorization framework is a planned addition.

Overall, the Model Gateway demonstrates how a well‑engineered MLOps platform—augmented with generative AI—can turn the traditionally cumbersome model‑management problem in drug discovery into a streamlined, scalable service, opening the door for faster, more reliable therapeutic innovation.

Authors

• Yan-Shiun Wu
• Nathan A. Morin

Paper Information

• arXiv ID: 2512.05462v1
• Categories: cs.SE, cs.DC, cs.LG, q-bio.QM
• Published: December 5, 2025
• PDF: Download PDF