Generate Biomedicines (GENB) investor relations material

Key technology and platform advancements

• Generative AI and agentic science have revolutionized protein therapeutic design since 2018, enabling rapid hypothesis testing and molecular design at unprecedented speed and scale.

• Integration of dry (computational) and wet (experimental) labs allows for a rapid feedback loop, now capable of building and testing up to 10 billion proteins in a week.

• Investment in cryo-EM and machine learning enables dynamic, atomistic-level understanding of protein interactions, feeding richer data into models.

• Data generation and experimental verification are now the primary differentiators, as model architectures become increasingly commoditized.

• The platform's evolution has shifted from optimizing known targets to tackling previously undruggable domains and complex biological functions.

Pipeline progress and clinical strategy

• Lead asset GB-0895, an anti-TSLP antibody for severe asthma, moved directly from phase I to phase III based on robust biomarker and safety data, leveraging model-informed drug development.

• The molecule demonstrated a 20-fold improvement in binding affinity, five-fold increase in preclinical potency, and an extended half-life, enabling a six-month dosing regimen.

• Strategic decision to retain full ownership of GB-0895 was driven by the need to prove the technology's clinical viability and maintain development speed.

• Second asset, GB-4362, targets neuropathy in cancer patients receiving ADCs, with Fast Track designation and potential for accelerated approval if proof of concept is achieved.

• Partnerships with major pharma (e.g., Amgen, Novartis) extend platform reach, with deals structured for upfront payments, milestones, and royalties.

Industry impact and future outlook

• AI-driven drug discovery is shifting from artisanal to engineering-based approaches, with data moats and proprietary experimental workflows as key sources of competitive advantage.

• The industry is at an inflection point, with scalable discovery and economies of scale challenging the traditional integrated pharma model.

• Future business models may see discovery-focused companies capturing greater value, while late-stage development and commercialization remain with large pharma.

• The ability to generate and measure new biological data at scale is expected to unlock new therapeutic domains and improve patient outcomes.

• Talent strategy centers on attracting interdisciplinary experts at the intersection of computation and biology, fostering innovation in both domains.