Improving Scientific Computing Efficiency in Pharma Using Cost-Optimized Cloud Infrastructure

Citation: Nivedha Sampath, "Improving Scientific Computing Efficiency in Pharma Using Cost-Optimized Cloud Infrastructure", Universal Library of Innovative Research and Studies, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article considers the pharmaceutical industry’s transition to cost-optimized cloud infrastructure for scientific computing as a strategic transformation of research processes. The relevance of the work is driven by the exponential growth in data volumes in molecular dynamics, genomics, and proteomics, as well as the need for accelerated training of large artificial intelligence models, which makes traditional enterprise clusters economically and technically inefficient. The study was framed as an effort to seek the models of consumption of cloud resources and hardware profiles that would lower costs while maintaining or improving computing performance for pharmaceutical science. The novelty in the article is in its absolute scrutiny of hybrid consumption models (spot, reserved, and serverless instances) to be used in combination with accelerators specialized in GPUs, TPUs, FPGAs, and quantum processors on one side and a systematized view of FinOps practices tying research tasks together with transparent cost allocation on the other. The main conclusion about efficiency is based on two factors: first, a very exact match between the hardware stack and workload profile; second, tight organization within the resource consumption model. Pharmaceutical companies can increase the speed of research without growth in total expenditures by justified use of next-generation GPU series and energy-efficient Arm hosts, plus specialized accelerators, combined with a distributed data storage inclusive network-flow control. Organizationally, the key factor is the introduction of continuous cost-optimization practices (FinOps) and iterative infrastructure scaling through pilot projects and declarative manifests. The article will be helpful to researchers in pharmaceutical science, cloud-platform engineers, and R&D leaders responsible for digitization strategy and reducing the costs of computing processes.

Keywords: Pharmaceutical Computing, Cloud Infrastructure, HPC, FinOps, Molecular Dynamics, Genomics, Proteomics, Artificial Intelligence, Cost Optimization.

https://doi.org/10.70315/uloap.ulirs.2025.0204006