A language layer for multi-modality discovery: Bridging biologics, ADCs, and peptides through structured data
Discovery teams are moving past standard monoclonals and attaching linkers, payloads, and peptides to antibody backbones. The software hasn’t kept up. Sequences sit in one system, chemistry in another, and conjugation details in notebook PDFs, so scientists spend their time hunting for data instead of designing experiments.
In this fireside chat, Trisha Pillay of Pharmatica speaks with Lily Helfrich, Product Manager of Scientific Modalities at Benchling, about what a fit-for-purpose data model looks like for biologics and hybrid modalities. We'll cover how a global biopharma replaced six legacy platforms with Benchling in 15 months, how the same foundation extends to ADCs and peptides, and why AI needs full molecule context to give answers you can trust.
Why legacy registries and point tools break when antibodies become conjugates, and what that costs your team in lost data lineage
Why building a registry in-house turns into technical debt, and what a shared scientific language layer does differently.
How one global biopharma moved more than 84,000 legacy antibodies into a single system with scientific context intact
What structured, connected data does for AI, from predictive models to teams of agents working across design, cloning, and analysis.
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