Model Gateway: Management Platform for Model-Driven Drug Discovery

Abstract:Pharmaceutical drug discovery demands machine learning (ML) infrastructure that goes beyond general-purpose Machine Learning Operations (MLOps): inference-time composition of multiple models for multi-parameter optimization (MPO), version management for physics-based models without serialized ML artifacts, enterprise compound library precomputation, and governance structured around scientific organizational units rather than generic access controls. No existing commercial or open-source platform simultaneously addresses this full set of requirements. This paper presents the Model Gateway, a cloud-based platform for managing machine learning and scientific computational models across drug discovery pipelines, providing centralized version control, pharma-structured governance, asynchronous execution, consensus model orchestration, automated retraining, and a unified application programming interface (API) service for heterogeneous clients including molecular design suites and Large Language Model (LLM) agents. In production at Eli Lilly, the platform governs more than 200 deployed models spanning small molecule, peptide, and antibody modalities and serves more than five downstream applications across all phases of the Design-Make-Test-Analyze cycle.

Software Engineering (cs.SE); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

H.3.4; I.2.1; I.2.11; J.3

arXiv:2512.05462 [cs.SE]

(or arXiv:2512.05462v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2512.05462 Focus to learn more arXiv-issued DOI via DataCite

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