Traditional data organization methods struggle to meet the scale and consistency required for collaborative efforts in drug discovery. To address this challenge and enhance data sharing and coordination, we developed DAIKON—a versatile open-source framework that seamlessly integrates targets, screens, hits, and project management within the realm of target-based drug discovery portfolios.
This framework’s knowledge capture components empower teams to document evolving molecule properties, foster collaboration through discussion threads, and incorporate visual modules that depict target progress through the pipeline. Serving as a repository, DAIKON draws data from Mycobrowser, UniProt, and PDB, aiming to harmonize diverse drug discovery programs globally while respecting local workflow nuances.
The application achieves modularity by abstracting the database, creating distinct layers for entities, business logic, infrastructure, APIs, and frontend. Docker facilitates the packaging of the framework into two solutions: daikon-server-core and daikon-client. Organizations have the flexibility to deploy the project on on-premises servers or VPCs, with Active Directory/SSO support for streamlined user administration. End users can conveniently access the application through a web browser.
It serves as a comprehensive tool for managing the drug discovery process, aiding from target identification to pre-clinical development.
The modular framework, initially tailored for Mycobacterium tuberculosis, extends beyond TB genes. Users can incorporate genes from different organisms by submitting a JSON document with gene data via an API call to the DAIKON Server. For TB genes, Mycobrowser supplies most organism and strain data, facilitated by the app’s automatic extraction, transformation, and loading “adapter.” A similar “adapter” for UniProt is in progress, broadening coverage across organisms, enabling users to directly import gene data from UniProt to DAIKON through searches and filters. Concurrently, we are refining the framework for customizable discovery pipelines in areas like SARS-CoV-2 and parasitic diseases. This includes standardized templates and ongoing efforts to integrate with molecular databases, in-app tools for compound clustering, and additional project management features for seamless implementation.
Currently, DAIKON is implemented within the TB Drug Accelerator program (TBDA) consortium but we have been refining and expanding its capabilities based on feedback from scientists. Our ultimate objective is to develop a versatile tool suitable for various workflow databases and projects, catering to both individual groups and broader collaborative efforts. The framework is freely available at https://saclab.github.io/daikon/
