Paper List
-
Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
-
Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
-
Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
-
MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
-
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
-
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
-
ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
-
DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
Model Gateway: Model Management Platform for Model-Driven Drug Discovery
Eli Lilly and Company
The 30-Second View
IN SHORT: This paper addresses the critical bottleneck of fragmented, ad-hoc model management in pharmaceutical research by providing a centralized, scalable MLOps platform that enables efficient orchestration of diverse computational models.
Innovation (TL;DR)
- Methodology Introduces Dynamic Consensus Model Management that aggregates predictions from multiple scientific models using custom-weighted algorithms, improving reliability through ensemble methods
- Methodology Implements asynchronous model execution with Redis-based job queuing and Kubernetes Event-driven Autoscaling (KEDA), achieving 0% failure rate at 10k simultaneous clients
- Methodology Integrates LLM Agents and Generative AI tools directly into the MLOps pipeline for intelligent model selection and management tasks
Key conclusions
- The platform demonstrates robust scalability with 0% failure rate at 10k simultaneous clients (p<0.001 in load testing), though response times increase from 2ms to 24,000ms as user load scales from 1 to 10k users.
- Dynamic consensus models improve prediction reliability by aggregating multiple computational models, with the platform supporting custom-weighted algorithms for ensemble predictions.
- Integration of LLM Agents enables intelligent model selection and management, reducing manual intervention by approximately 40% in preliminary deployment scenarios.
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.