Building Scalable Molecular Simulations in the Cloud

Running molecular dynamics at scale requires rethinking traditional approaches to computational infrastructure. At Purna, we have built a cloud-native simulation platform capable of executing millions of molecular evaluations per day. Here is how we approach the engineering challenges.

The Scale Challenge

A typical virtual screening campaign might evaluate hundreds of thousands of compounds against a protein target. Each evaluation involves molecular docking, scoring, and optionally short molecular dynamics simulations. Sequentially, this would take months. Our platform completes these campaigns in hours.

Architecture Overview

Our simulation infrastructure is built on three core principles:

1. Containerized Workloads

Every simulation runs in an isolated container with pinned dependencies, ensuring reproducibility across runs. We use lightweight containers optimized for GPU workloads, minimizing startup overhead to under 2 seconds per job.

2. Elastic GPU Scheduling

Not all molecular simulations require GPUs, and not all GPU workloads have the same requirements. Our scheduler intelligently routes jobs to appropriate hardware — CPU clusters for rapid scoring functions, single GPUs for molecular dynamics, and multi-GPU nodes for free energy perturbation calculations.

3. Streaming Results Pipeline

Rather than waiting for all simulations to complete before analysis, results stream into our data pipeline in real-time. This enables interactive exploration of results and early stopping when promising leads are identified.

GPU Optimization

The majority of our compute budget goes to GPU-accelerated molecular dynamics. Key optimizations include:

• Batched inference: Grouping similar-sized molecular systems to maximize GPU utilization
• Mixed precision: Using FP16 for force calculations where accuracy permits, with FP32 accumulation
• Memory-mapped trajectories: Streaming trajectory data to disk without blocking GPU computation

Cost Management

Cloud-native molecular simulation can be expensive without careful cost management. Our approach includes:

• Spot instance orchestration: Running fault-tolerant simulation batches on spot/preemptible instances with automatic checkpointing and restart
• Right-sizing: Profiling each simulation type to determine optimal instance sizes
• Result caching: Avoiding redundant computations by caching intermediate results with content-addressable storage

Lessons Learned

Building this platform taught us several lessons applicable to any large-scale scientific computing effort. First, reproducibility must be designed in from day one — it cannot be retrofitted. Second, observability is critical when running millions of independent jobs. Third, the boundary between ML inference and physics-based simulation is blurring, and infrastructure should accommodate both.

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Purna AI’s Molecular Intelligence Platform MIP is an AI-powered workspace for biology teams. It brings together molecular analysis, variant interpretation, protein structure prediction, and clinical database integrations into one environment. Built for teams who work with biological data and need consistent, reproducible answers without juggling disconnected tools. Learn more at purna.ai.