Part 1: Needs but also Challenges of Integrating AI in Chemical Research
Integrating AI technologies has become essential in today’s rapidly advancing fields of chemistry and materials research. Traditional experimentation methods are simply too slow to keep up with the demands of scientific discovery, with some projects taking years to yield results. Additionally, poor chemical lab data management can lead to a staggering 40% duplication of experiments, wasting valuable resources and scientists’ time. Communication barriers between data scientists and chemists further complicate projects; chemists often spend days or even weeks learning coding to communicate with data scientists, while data scientists struggle to grasp chemistry and biology concepts. Moreover, scattered databases make it difficult to access crucial information efficiently.
Part 2: So, What’s Pipeline Pilot?
Addressing these challenges, BIOVIA Pipeline Pilot was developed to simplify the AI integration process. Unlike traditional, code-heavy platforms, Pipeline Pilot offers an accessible, visual, no-code interface. This intuitive interface allows data scientists to create and share processes without requiring extensive programming knowledge from chemists and materials scientists, making it ideal for cross-functional teams in the Middle East and beyond.
Part 3: How Does It Work?
Pipeline Pilot includes pre-built components backed by scientific expertise across chemistry, biology, and materials science. Data scientists can also develop custom components using Python, R, and Jupyter Notebooks, enabling them to capture specialized knowledge for widespread, reusable applications. These components can be assembled into “scientific pipelines” that automate tasks from machine learning and image analysis to lab data processing.

Once created, chemists simply input their data and click “run” to use the pipeline, eliminating the need to understand complex Python scripts. The platform seamlessly integrates machine learning capabilities, allowing scientists to perform everything from model training to deployment without programming expertise. Additionally, the Pipeline Pilot web portal makes it easy to share research outcomes and protocols, promoting collaboration across teams.
Part 4: Conclusion
With Pipeline Pilot, scientists can stay hands-on with their research while harnessing the power of data science and machine learning in a code-free environment!
Refrences
Clement NARDARI, N. I. (2024, October). Solving Informatics Challenges with Pipeline Pilot: Present and Future . Dassault Systemes .
Dassault Systemes. (2024). BIOVIA. From BIOVIA Pipeline Pilot: https://www.3ds.com/products/biovia/pipeline-pilot