Benchling Acquires PipeBio to Enhance AI-Powered Antibody Discovery

Benchling, a cloud-based R&D software provider, has acquired PipeBio, a bioinformatics company specializing in high-throughput sequence analysis for biologics discovery. This collaboration brings together Benchling’s digital R&D tools with PipeBio’s sequence analysis platform to create a solution for antibody discovery and development.

Why PipeBio? As the demand for tools in antibody research grows, PipeBio’s platform is designed to handle large-scale sequence analysis, offering researchers capabilities to screen millions of sequences, visualize data, and identify candidates for development. Founded in 2020, PipeBio’s cloud-native product is used for biologics discovery, particularly in the early stages. Joining Benchling enables PipeBio to integrate additional resources and further develop its offerings within the biologics research space.

Complementary Strengths

• Benchling: Benchling offers R&D management tools such as electronic lab notebooks (ELNs) and lab information management systems (LIMS), aimed at helping scientists manage experiments, track data, and streamline workflows. The platform facilitates collaboration between experimental and computational research teams, connecting wet lab data with computational models.
• PipeBio: PipeBio’s platform supports sequence analysis and screening, allowing researchers to process large numbers of sequences and identify key candidates. Additionally, the platform integrates AI-driven models to assist in guiding antibody candidate selection and decision-making within biologics R&D.

With this acquisition, Benchling and PipeBio plan to enhance biologics research through an integrated platform that spans the entire discovery process:

• Sequence Intelligence: PipeBio’s capabilities in sequence analysis are intended to aid in the design and registration of complex large molecules, in line with trends in biologics R&D.
• Screening and Hit Selection: The integration will provide tools to connect sequence, structure, and function data, supporting researchers in selecting candidates for further development.
• AI-Enabled Discovery: The inclusion of AI models and integration capabilities aims to streamline the collaboration between experimental and computational teams, allowing predictions to inform the discovery and engineering process.

Image credit: Liudmyla Lishchyshyna

Topics: Tools & Methods