Embracing AI Transformation: Let’s Start with Data

The integration of artificial intelligence (AI) into early-stage drug discovery promises to accelerate hit discovery, optimization, and overall drug development processes. But how to onboard AI in your organization in a meaningful way? It starts with taking care of your data and articulating goals, first.

A recent article published in Nature Communications by Kristina Edfeldt et al. highlights the pivotal role of data management, dissemination, and AI integration within the Structural Genomics Consortium (SGC).

Drawing insights from this comprehensive roadmap, I highlithed the key points to consider when thinking about building/updating your organization’s data storage and processing principles:

1. Adhere to FAIR Principles: Ensure all data is Findable, Accessible, Interoperable, and Reusable. Implement standardized metadata schemas and persistent identifiers (e.g., DOIs) for all datasets to enhance findability and accessibility. Use interoperable data formats like XML or JSON to facilitate data exchange and integration.
2. Establish Precise Ontologies and Standardized Vocabulary: Define clear ontologies for data categorization, such as the BioAssay Ontology (BAO) for biological screening assays. Use standardized vocabularies like Medical Subject Headings (MeSH) to ensure consistency and improve machine readability.
3. Implement Centralized Database Architecture: Develop a unified data architecture using relational databases (e.g., PostgreSQL) or graph databases (e.g., Neo4j) to store and manage data. Ensure schema compatibility with established repositories like ChEMBL and PubChem to facilitate seamless data integration and dissemination.
4. Leverage Lab Automation and Integrated ELN/LIMS Systems: Utilize automation tools such as liquid handlers and robotic workstations to record detailed experimental metadata (e.g., reagent purity, ambient temperature). Integrate ELNs (e.g., LabArchives) with LIMS (e.g., LabWare) through APIs to streamline data capture and protocol linkage.
5. Promote Transparent and Reproducible Data Processing: Develop and publish open-source data processing pipelines using languages like Python and R. Document all preprocessing steps, including quality control measures, normalization techniques, and data transformation methods, in code repositories such as GitHub.
6. Create and Manage Multimodal Data Objects: Combine diverse data types (e.g., proteomics, genomics, chemical screening) into comprehensive data objects using data integration platforms like KNIME or Galaxy. Utilize BioCompute objects for tracking data processing pipelines and ensuring reproducibility.
7. Versioning and Archiving: Implement version control systems (e.g., Git) to track changes in datasets and maintain detailed change logs. Use data nutrition labels to summarize key characteristics, updates, and quality metrics for each dataset version.
8. Utilize Cloud-Based Data Hosting and Analysis: Leverage cloud platforms (e.g., AWS, Google Cloud, Microsoft Azure) for scalable data storage and computational resources. Employ the Model2Data approach by bringing analysis code to cloud-based data storage to minimize data transfer costs and enhance processing efficiency.
9. Engage in Active Learning and DMTA Cycles: Design data-driven feedback loops within the Design-Make-Test-Analyze (DMTA) cycles, using predictive models to guide experimental design. Implement active learning strategies to prioritize experiments that reduce prediction uncertainty and maximize data informativeness.
10. Foster Collaboration Between Experimentalists and Data Scientists: Promote a collaborative environment where experimentalists and data scientists work together from the onset of data generation. Incorporate data science into experimental design to enhance the impact and efficiency of research efforts, utilizing platforms like Jupyter Notebooks for shared analysis.

This article is from yesterday's newsletter "Weekly Tech+Bio Highlights #7 ."

Make sure to check it for the roundup of technology news, company annoucements and scientific breakthrous relevant to pharma and biotech research.

---

Welcome to my newsletter, "Where Technology Meets Biology"!

Here, I am sharing noteworthy news, trends, biotech startup picks, industry analyses, and interviews with pharma KOLs. Contact me for consulting or sponsorship opportunities here or at www.BiopharmaTrend.com .

Enjoying the newsletter? Subscribe to become part of the 15K+ readers here on LinkedIn. Please help us spread the word by sharing it with your colleagues and friends.

Also, consider joining my Substack community, where we are exploring a lot more (5.5K+ industry professionals are reading it via weekly email).

-- Andrii