Absci Investor Presentation Deck

BREAKTHROUGH IN DE NOVO DRUG CREATION Absci harnesses generative Al to lead a new paradigm of drug creation instead of drug discovery Unlocking de novo antibody design with generative artificial intelligence Amir Shanehsazzadeh*, Sharrol Bachas*, George Kasun, John M. Sutton, Andrea K. Steiger, Richard Shuai, Christa Kohnert, Alex Morehead, Amber Brown, Chelsea Chung, Breanna Luton, Nicolas Diaz, Matt McPartlon, Bailey Knight, Macey Radach, Katherine Bateman, David A. Spencer, Jovan Cejovic, Gaelin Kopec-Belliveau, Robel Haile, Edriss Yassine, Cailen McCloskey, Monica Natividad, Dalton Chapman, Luka Stojanovic, Rodante Caguiat, Shaheed Abdulhaqq, Zheyuan Guo, Katherine Moran, Lillian R. Klug, Miles Gander, Joshua Meier Absci Corporation, New York (NY) and Vancouver (WA), USA *Equal contribution Corresponding author ([email protected]) Abstract Generative artificial intelligence (AI) has the potential to greatly increase the speed, quality and controllability of antibody design. Traditional de novo antibody discovery requires time and resource intensive screening of large immune or synthetic libraries. These methods also offer little control over the output sequences, which can result in lead candidates with sub-optimal binding and poor developability attributes. Several groups have introduced models for generative antibody design with promising in silico evidence [1-10], however, no such method has demonstrated de novo antibody design with experimental validation. Here we use generative deep learning models to de novo design antibodies against three distinct targets in a zero-shot fashion where all designs are the result of a single round of model generations with no follow-up optimization. In particular, we screen over 400,000 antibody variants designed for binding to human epidermal growth factor receptor 2 (HER2) [11] using our high-throughput wet lab capabilities. From these screens, we further characterize 421 binders biophysically using surface plasmon resonance (SPR), finding three that bind tighter than the therapeutic antibody trastuzumab [12]. The binders are highly diverse and have low sequence identity to known antibodies. Additionally, these binders score highly on our previously introduced Naturalness metric [13], indicating that they are likely to possess desirable developability profiles and low immunogenecity. We open source the binders to HER2 and report the measured binding affinities. These results unlock a path to accelerated drug creation for novel therapeutic targets using generative AI combined with high throughput experimentation. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED "...no prior method has demonstrated de novo antibody design with experimental validation." Oxat K TM Y absci. 19

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