Advancing large-molecule discovery with a unified digital platform for data analysis and workflow management.

Area of Science:

• Biopharmaceutical drug discovery and development
• Computational chemistry and bioinformatics
• Digital transformation in life sciences

Background:

• The expanding repertoire of large-molecule treatments necessitates diverse and complex discovery and development workflows.
• Current manual, labor-intensive, and error-prone approaches involve numerous disparate software solutions for tasks like molecule registration, material tracking, and data analytics.
• This fragmentation hinders efficiency and increases the risk of errors throughout the drug development lifecycle.

Purpose of the Study:

• To introduce the concept of an integrated digital platform for large-molecule treatment discovery.
• To outline state-of-the-art concepts for automating and streamlining the entire discovery process.
• To demonstrate how a harmonized, open architecture can manage complexity from discovery to developability assessment.

Main Methods:

• Review of current state-of-the-art concepts in digital platforms for drug discovery.
• Conceptualization of a harmonized, open architecture integrating diverse workflows and hardware systems.
• Illustration of platform benefits and complexities through use cases like multi-specific antibodies and antibody-drug conjugates.

Main Results:

• The proposed integrated platform offers a transformative solution to the complexity of large-molecule treatment discovery.
• Shared workflows and the incorporation of artificial intelligence (AI) for predictive and generative tasks are key components.
• Examples demonstrate applicability across different use cases and maturity levels in biopharmaceutical development.

Conclusions:

• An integrated digital platform can significantly automate and streamline the discovery of new large-molecule treatments.
• This approach harmonizes complex workflows and hardware, improving efficiency and reducing errors.
• The platform facilitates advanced applications, including AI-driven drug design and development.