The journey from initial research to a viable drug is notoriously complex, resource intensive and time consuming. Traditional pipelines are often hindered by siloed expertise, with scientific innovation at one end, and practically minded engineers at the other. In today’s rapidly evolving pharmaceutical landscape, breaking down these silos isn’t just beneficial, it’s essential.  When done effectively, it can unlock unprecedented efficiency, scalability, and success in drug discovery.

Historically, drug development has been plagued by a fundamental disconnect between robust engineering and scientific flexibility. Engineers excel in creating scalable and reliable platforms, but these systems can struggle to accommodate the exploratory and non-deterministic nature of scientific research. Conversely, scientists produce innovative models and workflows that often lack the structure and versatility required for operational use. The result is a disjointed approach with systems that can’t quite keep up with discovery, and discoveries that can’t scale. It’s a gap that costs time, resources, and sometimes, real breakthroughs.

Consider a scenario where a biotechnology company invests in a state-of-the-art computational platform designed by an engineering firm. On paper, it’s a powerhouse. But in practice, its rigidity makes it hard for scientists to freely explore data or iterate at the pace discovery demands. The result? Low adoption, untapped potential, and a less efficient drug discovery process, delaying progress where it matters most: getting therapies to patients.

Achieving the best possible results in the current landscape requires a symbiotic relationship between science and engineering. By integrating domain expertise early, it’s possible to develop platforms that cater to the unique demands of scientific workloads while maintaining the stability, performance, efficiency, and dependability of a well-engineered system.

Scientific workloads differ dramatically from traditional IT systems.  They require non-linear scalability, dynamic resource allocation, and the ability to quickly adapt to shifting computational needs. Without proper orchestration, computer requirements are hard to predict, leading to idle resources in some cases and bottlenecks in others.  However, integrating an engineering driven approach, such as demand aware infrastructure design, can more effectively manage these complexities and optimize performance.

Another example is intelligent job scheduling, which considers resource usage, task urgency, and prioritization. By improving the accuracy of predicting the computational resources required for each task, it becomes easier to maintain system stability, even under heavy load. When the right expertise is brought in early, cloud infrastructure can be optimized for both performance and cost efficiency.

At bioX, these principles have led to significant results. In one instance, a Python script used for genetic analysis originally took three days to run. By collaborating closely with scientists, bioX engineers refactored the code, cutting the execution time down to just 11 minutes, a reduction of over 99%. By combining advanced mathematics with engineering optimization, they could improve productivity and gain quicker access to insights.

Similarly, our proprietary isGWAS algorithm delivered performance 800 times faster than existing tools. Through targeted engineering enhancements, that speed has now been pushed to an impressive 1500-fold increase. 

Neither of these gains would have been possible if bioX scientists and engineers weren’t working in perfect harmony. And to achieve that, a certain level of pragmatism is vital. For example, an experienced data engineer can identify and rewrite critical sections of scientific code to enhance its robustness without stifling the kind of flexibility that allows scientists to add maximum value.

As the pharmaceutical industry embraces digital transformation, breaking down silos between disciplines is essential. Properly integrating scientific and engineering expertise not only accelerates discovery timelines but also reduces costs and optimizes resources for greater success. By addressing the unique challenges of scientific workloads, it’s possible to significantly speed up the pace of early stage drug discovery.

This article is contributed by Manjula Dissanayake, Principal of Technology Innovation in Health Data Science at bioXcelerate AI.