Absci (ABSI) Goldman Sachs 45th Annual Global Healthcare Conference summary

Strategic evolution and technology platform

• Transitioned from scaling protein-protein interactions to AI-driven drug discovery, leveraging generative AI to design antibodies targeting hard-to-drug proteins like GPCRs and ion channels.

• AI platform uniquely enables epitope-specific antibody design, allowing rapid exploration of novel targets and unlocking new biology.

• Integrates wet lab and in silico methods, validating over 3 million AI-generated designs in six weeks, creating a fast active learning loop.

• Proprietary multimodal data (public and in-house) is central to model accuracy and competitive advantage.

• Data generation and validation, not compute, are the main bottlenecks in biological AI, differentiating from large tech competitors.

Business model and competitive positioning

• Adopted a hybrid model: developing an internal pipeline while maintaining pharma partnerships to maximize value retention and diversification.

• Internal pipeline assets can reach the clinic faster and at lower cost (e.g., TL1A with $13-15M investment vs. traditional $50-100M).

• Plans to partner internal assets from IND or phase 2, leveraging pharma's late-stage development strengths.

• Drug creation partnerships provide upfront payments, milestones, and royalties, with asset deals expected to yield higher returns.

• Recent $86M financing extends runway into 2027, with additional non-dilutive funding expected from new partnerships and asset transactions.

Pipeline, milestones, and future outlook

• Key upcoming milestones: TL1A NHP data (end of summer), clinic entry (H1 2025), phase I interim readout (H2 2025); ABS-201 drug candidate by year-end; ABS-301 in vivo validation by year-end.

• Four new partnerships anticipated this year, with strong pharma interest in lead assets.

• Target selection blends knowledge-based and AI-driven approaches, focusing on both best-in-class and first-in-class opportunities.

• M&A remains a tool for expanding capabilities, especially in wet lab technologies to enhance data generation.

• Aims to become a next-generation leader in AI-driven drug discovery, validated by both internal progress and major pharma collaborations.