Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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.