Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse

Paper in proceeding, 2025

Computer-aided drug discovery gradually builds on previous work and requires reusable code to advance research. Currently, research code is mainly used to provide further insights into the original research whilst code reuse has a lower priority. Modularity, the segmentation of code for independent modules, promotes good coding practices and code reuse. The registry pattern has been proposed as a way to call functionalities dynamically, but it is currently overlooked as a shortcut to promote code reuse. In this work, we expand the registry pattern to better suit computer-aided drug discovery and achieve a unified, reusable, and interchangeable interface with optional meta information. Our reformulated pattern is particularly suitable for collaborative research with standardized frameworks where multiple internal and external modules are used interchangeably and coding is more focused on fast iteration over low-debt technical code, such as in machine learning-based research for drug discovery. In a workflow, we exemplify the usage of the design patterns. Additionally, we provide two case studies where we 1) showcase the effectiveness of registration in a larger collaborative research group, and 2) overview the potential of registration in currently available open-source tools. Finally, we empirically evaluate the registry pattern through previous implementations and indicate where additional functionality can improve its use.