Cancer Research UK Drug Discovery Process Mining

TL;DR

This paper explores how process mining techniques can be applied to event logs from Cancer Research UK's drug discovery process to improve operational efficiency and accelerate the development of new treatments.

Contribution

It evaluates methodologies for creating process models from event logs in drug discovery, highlighting tools like Disco and ProM for different expertise levels.

Findings

Process mining can reveal valuable insights into drug discovery workflows.

Disco is suitable for management-level analysis without deep technical knowledge.

ProM is ideal for detailed research-oriented process analysis.

Abstract

Background. The Drug Discovery Unit (DDU) of Cancer Research UK (CRUK) is using the software Dotmatics for storage and analysis of scientific data during drug discovery process. Whilst the data include event logs, time stamps, activities, and user information are mostly sitting in the database without fully utilising their potential value. Aims. This dissertation aims at extracting knowledge from event logs data which recorded during drug discovery process, to capture the operational business process of the DDU of Cancer Research UK (CRUK) as it was being executed. It provides the evaluations and methodologies of drawing the process mining panoramic models for the drug discovery process. Thus by enabling the DDU to maximise its efficiency in reviewing its resources and works allocations, patients will benefit from more new treatments faster. Conclusion. Management of organisations can be benefit from the process mining methodologies. Disco is excellent for non-experts on management purposes. ProM is great for expert on research purposes. However, the process mining is not once and for all but is a regular operation management process. Indeed, event logs needs to be understand more on the target organisational behaviours and organisational business process. The researchers have to be aware that event logs data are the most important and priority elements in process mining.