74th ASMS Conference on Mass Spectrometry and Allied Topics

Web-Based In-Silico Mass Spectral Fragmentation Using Scalable Cloud-Orchestrated Spectrus JS Technology /Scalable Web-Based In Silico MS Fragmentation / Enabling In Silico MS/MS Fragmentation Through a Scalable Web Platform

Richard Lee, Austin Blue, Ryan Andrews, Anne Marie Smith, Sofya Chudova, Artyom Petrovskiy, Vitaly Lashin, Rostislav Pol

In-silico fragmentation is a critical component of mass spectrometry data interpretation, enabling structure elucidation, annotation, and automated workflows. Traditional desktop-based fragmentation tools are limited by local compute resources, deployment complexity, and scalability constraints. To address these limitations, we present a web-based in-silico fragmentation platform built on the Spectrus JS framework, delivering scalable, browser-based workflows. The platform integrates a deep rules-based approach to deliver chemically informed predictions in a modern web environment, supporting high-throughput use cases, collaborative analysis, and seamless integration with existing informatics pipelines, while maintaining performance, reliability, and reproducibility.

The platform is implemented using the Spectrus JS (JavaScript) framework, using micro-service like architecture and cloud-hosted computation services. In-silico fragmentation is performed using an established rule-based engine. Fragmentation jobs are submitted via RESTful APIs and executed in containerized environments to enable scaling. A cloud orchestration layer manages job queuing, resource allocation, and fault tolerance. Results are returned asynchronously to the client and visualized interactively in the browser. The architecture supports integration with databases and reporting workflows.

Initial deployment demonstrates that web-based in-silico fragmentation can achieve performance and reliability comparable to traditional desktop implementations, while enabling significant gains in scalability and accessibility. Fragmentation jobs spanning single compounds to large molecular batches were executed concurrently without degradation in prediction fidelity. The implementation of containerized deployment enables dynamic scaling in response to workload demand, reducing latency for high-throughput scenarios.

Fragmentation outputs generated via the web platform were consistent with those produced by established desktop routines, confirming preservation of chemical intelligence and rule execution.

The Spectrus JS front end provided real-time visualization of fragment ions, neutral losses, and structure–spectrum relationships directly within the browser, eliminating the need for local software installation. System monitoring showed efficient orchestration of computer resources, with automated recovery from transient failures. These results demonstrate the feasibility of deploying chemically rigorous in-silico fragmentation as a scalable, cloud-native service suitable for modern mass spectrometry informatics workflows.