Absci Investor Presentation Deck

CASE STUDY: DESIGNING BETTER HER2 BINDERS Al quantitatively predicts antibody affinity Predicted ACE score 9 8- 7 6 LO 5 4 3 2 Pearson R= 0.97 Spearman p= 0.84 RMSE= 0.42 3 4 5 6 Measure ACE score 7 8 9 HIGH PREDICTIVE PERFORMANCE Pearson R correlation of 0.93 - Trained on 90% of dataset - Results shown for 10% of dataset not seen by model Pearson R Spearman p RMSE 0.4 0.6 0.8 Measured replicate 1 vs Measured replicate 2 Measured vs Predicted same replicate Measured vs Predicted different replicates 0.0 0.2 HIGH QUALITY DATA Models trained on one replicate can predict unseen data from a different replicate 1.0 Count 20 15 10 LO 5 0 7.50 Bachas, S., Rakocevic, G. et al., "Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness," 2022 pre-print in bioRxiv. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Affinity-optimized Trastuzumab 7.75 8.00 8.25 8.50 8.75 9.00 9.25 9.50 - log10 (KD) HIGH AFFINITY PREDICTIONS Models can find variants with higher affinity than seen in training data - 92 of top 100 predicted high-affinity variants bind tighter than trastuzumab absci. 23

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