#1model from absci import de_novo_model de_novo_model.load_latest() antigen model.load_pdb("7olz.pdb", chain="A") antibodies = model.predict(antigen, N=300000) DRUG CREATION absci. from absci_library import codon_optimizer library CORPORATE PRESENTATION SEPTEMBER 2023 ABSCI CORPORATION 2023 ALL RIGHTS RESERVED = codon_optimizer.reverse_translate(library) library.to_csv("covid-antibody-designs.csv") from absci import lead_opt_model lead_optimizer = lead_opt_model.load_latest() library.naturalness = lead_optimizer.naturalness(library) library.to_wet_lab(assay="ACE") lead_optimizer.optimize(library).to_wet_lab(as say="SPR") from absci import genetic_algorithm; parameters=["maximizelbinding_affinity:pH=7.5", "minimizelbinding_affinity: pH-6.0", "maximize l human_naturalness"]; library = genetic_algorithm.multiparametric_optimization (library, parameters, evolutions=100); library.to_wet_lab(assays=["ACE", "SPR", "Bioassays"])#2Disclaimers Forward-Looking Statements Certain statements in this presentation that are not historical facts are considered forward-looking within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements containing the words "will," "may,” “anticipates,” “plans,” “believes,” “forecast,” "estimates," "expects," "predicts," "advancing,” “aim,” “potential,” and “intends," or similar expressions. We intend these forward-looking statements, including statements regarding our strategy, financial performance and results of operations, including our expectations and guidance regarding cash, cash equivalents and restricted cash, our projected cash usage, needs and runway, future operations, future financial position, future revenue, internal research and technological development activities, the effective incorporation of our technology in drug design and discovery to accelerate drug development and increase probability of success, advancements toward in silico drug design and creation, research and technology development collaboration efforts, growth plans, projected costs, prospects, plans and objectives of management, to be covered by the safe harbor provisions for forward-looking statements contained in Section 27A of the Securities Act and Section 21E of the Securities Exchange Act, and we make this statement for purposes of complying with those safe harbor provisions. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies, and prospects, which are based on the information currently available to us and on assumptions we have made. We can give no assurance that the plans, intentions, expectations, or strategies will be attained or achieved, and, furthermore, actual results may differ materially from those described in the forward-looking statements and will be affected by a variety of risks and factors that are beyond our control, including, without limitation, risks and uncertainties relating to the development of our technology, our ability to secure milestone payments and royalties, and our ability to effectively collaborate on research, drug discovery and development activities with our partners or potential partners; along with those risks set forth in our most recent periodic report filed with the U.S. Securities and Exchange Commission, as well as discussions of potential risks, uncertainties, and other important factors in our subsequent filings with the U.S. Securities and Exchange Commission. Except as required by law, we assume no obligation to update publicly any forward- looking statements, whether as a result of new information, future events, or otherwise. Market and Statistical Information This presentation also contains estimates and other statistical data made by independent parties and by us relating to market size and growth and other industry data. These data involve a number of assumptions and limitations, and you are cautioned not to give undue weight to such estimates. We have not independently verified the data generated by independent parties and cannot guarantee their accuracy or completeness. Trademark usage This presentation/document/webpage contains references to our trademarks and service marks and to those belonging to third parties. Absci®, absci., SoluPro® and SoluPure® are Absci registered trademarks with the U.S. Patent and Trademark Office. We also use various other trademarks, service marks and trade names in our business, including the Absci logo mark (2), the Absci Al logo mark (ai), the Unlimit with us mark (Unlimit With Us"), the unlimit symbol (unlimit), Bionic protein, Bionic Enzyme, Bionic Antibody, Bionic SoluPro, Denovium, Denovium Engine, Drug Creation, Integrated Drug Creation, HiPrBind, HiPrBind Assay, Translating Ideas into Drugs, Translating Ideas into Impact, We Translate Ideas into Drugs, Creating drugs at the speed of Ai, Better biologics for patients, faster, Breakthrough therapeutics at the click of a button, for everyone, and We Translate Ideas into Impact. All other trademarks, service marks or trade names referred to in this presentation/document/webpage are the property of their respective owners. Solely for convenience, the trademarks and trade names in this presentation/document/webpage may be referred to with or without the trademark symbols, but references which omit the symbols should not be construed as any indicator that their respective owners will not assert, to the fullest extent under applicable law, their rights thereto. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED absci. 2#3S www What if the next transformative drug was not discovered. but created at a click of a button? ABSCI CORPORATION 2023 ALL RIGHTS RESERVED absci. 3#4THE PROBLEM - CURRENT NEED FOR GENERATIVE AI The drug discovery paradigm is ripe for disruption 5.5 YEARS FROM DISCOVERY TO IND OPTIMIZE FOR AFFINITY OPTIMIZE FOR TOXICITY ABSCI CORPORATION 2023 ALL RIGHTS RESERVED OPTIMIZE FOR DEVELOPABILITY SUBOPTIMAL CANDIDATE Long iterative process resulting in drug candidates with suboptimal attributes <5% SUCCESS RATE FROM DISCOVERY TO LAUNCH CHI 0 IID absci 4#5WHY HASN'T GENERATIVE AI TRANSFORMED BIOLOGIC DRUG DISCOVERY? Unlocking the potential of generative Al in biology requires scalable biological data Small Molecule V. Biologic .. ●● ●● ●● .. ●● ●● .. ●● ●● [.. ●● ●● ●● .. .. .. ●● ●● ●● ●● ●● Extensive Libraries Y ●● ●● ●● ●● ●● .. ●● ●● [.. ●● ABSCI CORPORATION 2023 ALL RIGHTS RESERVED ●● ●● 00 ●● ●● ●● ●● ●● ●● ●● Limited Data Biologics require living organisms to produce drug variants for testing ●● ●● ●● ●● ….. ●● es .. ●● ●● .. ●● ●● .. ●● ... ●● ●● ●● ●● ●● ●● ●● 00 ●● Consistency and accurate data is limited Unlocking the potential of generative Al in biology... a. WET LAB ●● ●● ●● ●● ●● ●● .. .. .. 414 ●● .. 00 ●● ●● ●● ●● ●● ●● AI ●● ●● ●● ●● ●● ●● ●● ...requires generating scalable biological data absci. 5#6BIOTECH INDUSTRY INFLECTION POINT Absci is solving the problem of scalable biological data enabling true generative Al for biologics drug discovery Absci's E.coli SoluPro™ cell line Billions of cells, expressing proteins of interest ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Absci's ACE Assay™ screens billions of unique drug variant candidates 2009 29999 0.000 200000 00000 ●●●●● NON-BINDING . TIGHT BINDING High-quality data flows into Absci's generative Al engine ai absci. 6#7roved, 11.2% of drugs entering clinical trials approved 2006: 22 approved, 11.2% 2007: 18 approved, 10.7% 2008: 24 approved, 6 approved, 7.8% 2010: 21 approved, 6.8% 2011: 35 approved, 6.1% 2012: 39 approved, 5.3% 2013: 27 approved, 5.2% 2014: 41 7% 2015: 45 approved, 13.8% ABSCI CORPORATION 2023 ALL RIGHTS RESERVED in DUR- OUR = Instead of finding the needle in the haystack, Absci is creating the needle ME Measurement Rate mass - OD Vol. pt ↳ Weigh 8/2-hr Boma the Specific poltivity Resus. D DBII ASI ASI 96w 19 384 w 7 The incub 384 W Į seal pla ↓ Source Spin 51 ✓ 4°C Incubat absci. 7#8The Solution At Absci, the future is now with our Integrated Drug Creation™ platform DATA TO TRAIN Proprietary wet-lab assays capable of generating billions of protein-protein interactions a week for ML training WET LAB TO VALIDATE Scalable wet-lab infrastructure capable of validating 2.8 million unique Al-generated designs a week ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Sle AI TO CREATE Generative Al engine to create new antibodies and next-gen biologics absci. 8#9ABSCI IS THE LEADER IN GENERATIVE AI DRUG CREATION FOR BIOLOGICS Cycles completed within weeks Absci's rapid cycle times allow for: ABSCI CORPORATION 2023 ALL RIGHTS RESERVED DATA TO TRAIN Typical 6-week per cycle WET LAB TO VALIDATE 01 Rapid iteration and improvement of Al models 02 Reduction of preclinical development timelines and increased probability of success 03 AI TO CREATE Accelerated achievement of mission and attraction of top Al talent absci. 9#10ABSCI'S END-TO-END PLATFORM SOLUTION The leading full-stack Al platform for biologics drug creation Find patient cohort specific targets ↓ Novel target discovery ai. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Discover novel antibodies for selected target De novo discovery in silico ai. Optimize antibodies' characteristics 1. Lead optimization in silico p ai. Incorporate site-specific chemical handles Bionic SoluPro™ strain OPTIMAL DRUG CANDIDATE absci. 10#11て BOXER AMAS Absci is the first to design and validate novel antibodies using zero-shot generative Al Zero-shot: a machine learning technique in which a model is trained to recognize and classify new objects without explicitly being trained on those objects' examples. For antibodies, this means designing an antibody to bind to an antigen with no previous demonstrations of binders to said antigen. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED absci. 11#12De novo design in silico De novo drug creation with 'zero- shot' generative Al Target antigen structure US De novo antibody designs YY YYYYYY Y YYYY YYYYYY ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Target epitope Wet lab assays ACE assay data Antibody scaffold sequence SPR assay data ai. Validated de novo binders ی و دیگری دارد که m absci. 12#13DE NOVO DESIGN IN SILICO REQUIRES LOTS OF HIGH-QUALITY TRAINING DATA Highly validated ACE Assay generates high- quality and high-throughput data to train deep learning models 1. Strains expressing unique antibody sequence variants 2. Fix and permeabilize cells and add labeled probes ³00+ ¯¯TT Labeled scaffold-binding protein reports specifically on titer Labeled antigen reports on affinity 3. Screen and sort by flow cytometry 4. NGS Expression Binding M M M M Labeled probes 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 5. ACE Assay scores (binding classification) HCDR123 score 95.3% Metric Accuracy Precision Recall F1-score HCDR3 score 95.52% 95.39% 94.83% 0.9511 95.65% ● • ~800 positive controls 92.91% 0.92426 ~5K controls are spiked into libraries of 400k in size: • ~1000 negative controls ● Each included with multiple codon optimized variants absci. 13#14CASE STUDY: DE NOVO DESIGN IN SILICO Laboratory validation straight out of the model; 421 designs confirmed by SPR We selected trastuzumab, which binds HER2, as a scaffold to test the HCDR3 predictions The model is conditioned with HER2 3D structure and trastuzumab scaffold excluding HCDR3 designs E Antibody scaffold sequence Any antibody known to bind to HER2 or any homolog (>40% sequence identity or part of the same homologous superfamily) to HER2 is removed ABSCI CORPORATION 2023 ALL RIGHTS RESERVED ● ● ● ● Count 100 80 60 40 20 Trastuzumab 7 -log₁K (M) 440,354 antibody variants designed Approx. 4,000 estimated binders by ACE Assay 6 421 confirmed by SPR 71 exhibit <10 nM affinity 3 bind tighter than WT trastuzumab absci. 14#15CASE STUDY: DE NOVO DESIGN IN SILICO Al designs of all HCDRS achieve high binding rates and outperform biological baselines Zero-shot de novo generated Human HER2 Rat HER2 HER3 Matched input antigen Mis-matched input antigen Mis-matched input antigen Mis-matched input antigen ABSCI CORPORATION 2023 ALL RIGHTS RESERVED VEGF Biological baseline OAS OAS-J SAbDab HER2 Binding Rate (%) measured via ACE assay HCDR3 HCDR123 Random baseline Permuted sequences 10.6 2.8 2.9 2.5 2.68 5.25 3.16 0.33 1.8 0.5 0.2 0.0 0.16 0.32 0.06 N/A Al designs are specific Inputting a mis-matched undesired antigen (e.g., Rat HER2, HER3, VEGF) into the model results in significant performance decrease towards desired antigen ● ● Al models outperform biological baselines De novo designed HCDR3s achieve a 4-fold improvement over random OAS baseline ● Indicates the model's use of antigen information for sequence designs ● De novo designed HCDR123s achieve an 11-fold improvement over random OAS baseline absci. 15#16CASE STUDY: DE NOVO DESIGN IN SILICO High sequence diversity supports patent estate expansion and differentiation HCDR3; HCDR3; Designs are sequence diverse from one another, with a mean edit distance of 7.7 ± 2.1 SD ABSCI CORPORATION 2023 ALL RIGHTS RESERVED 14 12 10 CO st Edit distance between pair of HCDR3s Example HCDR3 designs (edit distance) -Log₁0 (KD) SRWG GD GFYAMDY (Wildtype) 8.71 ARWGNYYYYM DY (6) 8.77 ARYYYGFYYFDY (7) 8.92 6.24 9.03 7.0 8.4 7.01 8.02 ARYAGVERPGSFAY (11) TRYFFNGWYYFDV (9) AFADS GAYGIWSF (12) ANDIYIQ GYDLNR (12) ARGYSGDW PYET FYV (10) ARYD Y GYYIY VS (10) Key: Amino acids of the same color belong to the same class absci. 16#17CASE STUDY: DE NOVO DESIGN IN SILICO 'Zero-shot' designs of new antibodies from scratch using generative Al ● Zero-Shot: Model has never seen a binder to target or homologs • Binders were identified straight out of the model - no lead optimization was performed • Predicted structures reveal meaningful biological interactions • Demonstrated across four therapeutic targets: HER2, VEGF-A, COVID omicron, undisclosed target ABSCI CORPORATION 2023 ALL RIGHTS RESERVED HER2 ANTIGEN HEAVY CHAIN HCDR3 LIGHT CHAIN TRASTUZUMAB IMGT RESI106 SIDE CHAIN TRASTUZUMAB IMGT RESI107 SIDE CHAIN TRASTUZUMAB IMGT RESI109 SIDE CHAIN TRASTUZUMAB IMGT RESI113 SIDE CHAIN TRASTUZUMAB IMGT RESI117 SIDE CHAIN TRASTUZUMAB HCDR3S BACKBONE DE NOVO HCDR3S BACKBONES absci. 17#18DE NOVO ANTIBODY DESIGN IN SILICO In silico validation of Zero-shot designs toward diverse targets Al-generated Heavy Chain Al-generated Light Chain Known binder structure¹ Antigen ¹Known binder crystal structures are literature sourced Prostaglandin E receptor EP4, GPCR, Fab (5YHL) ABSCI CORPORATION 2023 ALL RIGHTS RESERVED 윤 S. aureus a-hemolysin monomer, Fab (4IDJ) P. vivax RBP2b, monoclonal (6WOZ) Pekin duck egg lysozyme, Fab (5VJQ) Human urokinase receptor (2FD6) absci. 18#19BREAKTHROUGH 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#20AI DRIVEN LEAD OPTIMIZATION Multiparametric Al lead-optimization can enable higher potential therapeutics and increased Pos Find patient cohort specific targets ↓ Novel target discovery ai. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Discover novel antibodies Optimize antibodies' for selected target characteristics ↓ De novo discovery in silico EB ai. ↓ Lead optimization in silico y ai. Incorporate site-specific chemical handles ↓ Bionic SoluPro™ strain OPTIMAL DRUG CANDIDATE absci. 20#21CASE STUDY: AI-DRIVEN LEAD OPTIMIZATION Multiparametric Al lead-optimization for increased success rates & higher potential therapeutics Yo INITIAL LEAD CANDIDATE ai. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Property 1 Property 3 Property 2 Higher potential therapeutics faster Increased probability of success Affinity tailored for desired application Higher Developability ● • Thermostability ● ● ● ● ● ● • Self-association ● Lower immunogenicity • S.c. formulation Higher expression levels enabling lower COGS • Higher potential with novel biology Dual- or multi-valent binding Conditional biologics ● absci. 21#22CASE STUDY: DESIGNING BETTER HER2 BINDERS Al models expanded search space by orders of magnitude yo CDR1 CDR2 CDR3 Amino acids ARNDOEG Heavy chain detail CDR1 CDR2 OOOOOOOOOO OOOOOOOOOO OOOOOOOOOO Heavy Chain Light Chain ABSCI CORPORATION 2023 ALL RIGHTS RESERVED EPSTWYV & JI OOO CDR3 OOOOOO DOOO OOO All possible triple mutants of 19 amino acids in 20 positions (HCDR2 & HCDR3) Sampled space. (50k Variants) Hypothetical sequence space 7x 106 ● ● Bachas, S., Rakocevic, G. et al., "Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness," 2022 pre-print in bioRxiv. Combinatorial mutagenesis of up to 3 mutations over ten amino acids each in HCDR2 and HCDR3 Sampled less than 1% of the sequence space Measured binding affinity of nearly 50,000 sequence variants absci. 22#23CASE 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#24Higher naturalness improves probability of success and expression levels ai. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED REDUCED MA IMMUNOGENICITY 380 INCREASED DEVELOPABILITY HIGHER TITER Optimizing for naturalness with Absci's proprietary Al model to overcome major challenges in antibody development absci. 24#25CASE STUDY: OPTIMIZING HER2 BINDERS Simultaneous co-optimization of affinity and naturalness Predicted Affinity Score 9 1 0 Maximum 5 10 Generation Affinity Objective Minimum Maximum trastuzumab 15 20 Target Maximize, minimize, or tailor binding affinity Naturalness 0.35 0.30 0.25 0.20 0 Maximum 5 10 Generation Affinity Objective Minimum Maximum trastuzumab 15 Target At the same time, ensure sequences appear to come from humans (naturalness) 20 Naturalness 0.35 0.30 0.25 0.20 0.15 0.10 2 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 Best Predicted Variants Training Variants Trastuzumab 4 6 Affinity Score Models simultaneously tuning for affinity & maximizing naturalness 8 absci. 25#26CASE STUDY: AI-DRIVEN LEAD OPTIMIZATION 85% of Top 100 "natural" Trastuzumab variants exhibit higher-affinity than wild-type Count 20.0 17.5 15.0 12.5 10.0 7.5 5.0 2.5 0.0 Designed Variants Trastuzumab 7.6 8.2 8.4 8.6 8.8 -log10 (KD) 85% of top binders have higher affinity than Trastuzumab 7.8 8.0 9.0 ■ Al predicts the affinity of unseen variants from libraries generated using diverse mutational strategies and combinatorial sequence space Al models make predictions with actionable performance using <0.1% of the combinatorial sequence space as training set ■ Naturalness is associated with developability metrics and expression titer ▪ Enables one-shot multiparametric lead optimization potentially accelerating time to clinic 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 absci. 26#27AI-DRIVEN LEAD OPTIMIZATION Al-optimization for dual- or multi- valent biologics increases potential PRECLINICAL DEVELOPMENT Cross-species binding for improved success rates and speed G HUMAN-AG MOUSE-AG G CYNO-AG ABSCI CORPORATION 2023 ALL RIGHTS RESERVED IMMUNOLOGY Increased efficacy by simultaneous binding to multiple desired isoforms ELVE ISOFORM 1 18 ISOFORM 2 ISOFORM 3 INFECTIOUS DISEASES Broad spectrum antibodies with simultaneous binding to multiple viral variants VARIANT 1 VARIANT 2 VARIANT 3 absci. 27#28NOVEL TARGET DISCOVERY The leading Al platform for Al-enabled biologics drug creation Find patient cohort specific targets ↓ Novel target discovery ai. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Discover novel antibodies for selected target ↓ De novo discovery in silico ai. Optimize antibodies' characteristics ↓ Lead optimization in silico ai. y Incorporate site-specific chemical handles ↓ Bionic SoluPro™ strain OPTIMAL DRUG CANDIDATE absci. 28#29Leveraging exceptional immune responses to identify new potential cancer specific targets and therapeutics ABSCI CORPORATION 2023 ALL RIGHTS RESERVED absci. 29#30Antibodies selected in tertiary lymphoid structures bind to cancer cells and are associated with favorable clinical outcomes B cell Follicular dendritic cell 10 74 Tumor Plasma cell T cell Macrophage LO Tertiary Lymphoid Structures (TLS) are centers of immune activity (B-cell proliferation and antibody production) that develop in chronically inflamed tissues such as tumors. 00 Meylan, Maxime, et al. "Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer." Immunity 55.3 (2022): 527-541. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED ● Progression-free (%) 100 75 50- 25- 000000 P= 0.019 10 Time High Ig Staining +++ Low Ig Staining 15 20 The presence of TLS is associated with longer progression-free survival and better response to immune checkpoint inhibitors. • Rapidly growing evidence illustrates correlation between TLS- derived antibodies in the tumor microenvironment and positive clinical outcomes. ● • TLS-derived antibodies have been shown to be associated with apoptosis of cancer cells in patients. absci. 30#31Our integrated workflow identifies the antigens targeted by exceptional immune responses Samples collected from patients with exceptional immune response Antibody expression ABSCI CORPORATION 2023 ALL RIGHTS RESERVED 37 Immunoglobulin (Ig) reads from RNAseq data Target identification using high throughput proteomics LIGHT CHAINS HEAVY CHAINS Assembled Ig chain sequences Target antigen confirmed through SPR or BLI P Computationally reconstructed antibodies Ve Pang Fully human antibody and target antigen identified absci. 31#32Absci's workflow identifies antigens targeted by exceptional immune responses A Highly Productive Workflow* ● >6,600 Reconstructed antibodies >250 Hits under evaluation *Information as of 02/09/2023 ABSCI CORPORATION 2023 ALL RIGHTS RESERVED 22 Antibody/Target pairs Expanded network of health care institutions provides continuous access to patient data (melanoma, colorectal cancer, RA, psoriasis, lupus, etc.) Validated examples in oncology, immunology and infectious diseases Large collection of TLS-derived antibodies targeting cancer specific antigens, cytokine receptors, checkpoint inhibitors and other targets Immuno-oncology 26% 35% Oncology 39% Autoimmunity and Inflammation absci. 32#33NOVEL TARGET DISCOVERY Absci partners with leading health institutions to drive novel target discovery PARTNERING WITH TOP- CLASS HEALTH INSTITUTES ● ● ● Aster Insights Avera Health Saint John's Cancer Institute Department of Translational Molecular Medicine University of Oxford Others in progress ABSCI CORPORATION 2023 ALL RIGHTS RESERVED PROVIDES ABSCI WITH • access to data from thousands of patients across relevant oncology and immunology indications • continuous funnel of data for the discovery of novel disease-relevant targets and antibodies absci 33#34ABSCI'S END-TO-END PLATFORM SOLUTION The leading Al platform for Al-enabled biologics drug creation Find patient cohort specific targets ↓ Novel target discovery ai. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Discover novel antibodies for selected target ↓ De novo discovery in silico EB ai. Optimize antibodies' characteristics ↓ Lead optimization in silico ai. y Incorporate site-specific chemical handles Bionic SoluPro™ strain OPTIMAL DRUG CANDIDATE absci. 34#35NON-STANDARD AMINO ACID INCORPORATION Bionic™ protein technology: non-standard amino acid (nsAA) incorporation Absci's high-yielding Bionic SoluPro™ strain enables selective site-specific nsAA incorporation into difficult-to-produce biologics (proteins, enzymes, mAbs, fabs, VHHs) Bionic™ protein technology enables: Rapid assessment of payload location • Precise control over payload location • Uniform and homogenous Drug-Antibody-Ratio (DAR) for ADCs • Attachment of diverse chemical moieties for novel applications ● ABSCI CORPORATION 2023 ALL RIGHTS RESERVED EXPRESSION OF MAB IN BIONIC SOLUPRO" CELL LINE (ADC PRECURSOR) mAb 000 nsAA Bionic SoluPro™ strain & process pAcF - p-acetyl-phenylalanine pAzF - p-azido-L-phenylalanine oPrY -o-propargyl-L-tyrosine ADC precursor mAb TM absci. 35#36NON-STANDARD AMINO ACID INCORPORATION Unlocking new molecular functionalities and application Bionic SoluPro™ platform enables site-specific non-standard amino acid incorporation into difficult-to-produce biologics ● • designed for maximum incorporation efficiency ● Unlocks functionalities such as chemical modification, drug conjugation, pegylation, glycosylation ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Use in wide range of applications • development of ADCs with improved therapeutic properties (pharmacokinetic, efficacy, and safety profiles) half-life extension ● • site-specific, homogeneous, designer glycosylation attachment of novel chemical moieties enzyme immobilization/modification ● absci. 36#37NON-STANDARD AMINO ACID INCORPORATION Bionic SoluPro™ a specialized E. coli cell line for non-standard amino acid incorporation Bionic SoluPro™ cell line Patented E. coli cell line bioengineered for production of mammalian proteins and site-specific incorporation of non-standard amino acids ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Semi-oxidized cytoplasm Engineered redox environment to achieve scalable, soluble protein production Precise expression control SoluPro™ cell lines achieve precise control over induction through genetic engineering of metabolic pathways and proprietary plasmid designs nsAA incorporation Optimized for high-efficiency incorporation of non-standard amino acids absci. 37#38CASE STUDY: MULTIPLE INSERTIONS OF NON-STANDARD AMINO ACIDS Increased drug to antibody ratio with the incorporation of multiple nsAAs We investigated our ability to produce bionic Trastuzumab with 1, 2, or 3 nsAA incorporation sites H289 1257 A166 A122 *DAR = Drug-to-Antibody Ratio ABSCI CORPORATION 2023 ALL RIGHTS RESERVED 1x nsAA = DAR 2 2x nsAAS = DAR 4 3x nsAAs = DAR 6 Relative expression II 2 x nsAA O nsAA (wt) OOOO OOOO OOOO 1 x nsAA 3 x nsAA 3 x nsAA (alt) O OOOO Bionic SoluPro™ has been able to incorporate a non- standard amino acid into a total of 3 sites of a heavy chain mAb without major reductions in titer absci. 38#39CASE STUDY: CONJUGATING TO NON-STANDARD AMINO ACIDS Easy conjugation producing homogenous drug substances Intensity 1.0E+04 8.0e+03- 6.0E+03. 4.0E+03 2.0E+03. 0.0e +00 Conjugated Bionic™ trastuzumab Bionic™ trastuzumab 135k ABSCI CORPORATION 2023 ALL RIGHTS RESERVED 140k 145k Mass (Da) Successful conjugation of payload 150k 155k absci. 39#40VALUE CREATION FOR PATIENTS AND PARTNERS - TODAY Unlocking new and differentiated Higher Potential Biologics with Increased Pos Multidimensional optimization in parallel creates higher quality biologics with an increased Probability of Success SELECTIVITY NATURALNESS AFFINITY ABSCI CORPORATION 2023 ALL RIGHTS RESERVED value drivers Reducing Time & Increasing Competitiveness Drug creation process significantly shortened reducing research costs and increasing competitiveness 414 Novel biology: Multi- valent biologics & conditional biologics Preclinical development: Cross-species binding to improve success rates & speed MOUSE-AG HUMAN-AG ( CYNO-AG Broadening Intellectual Property Space Al-driven drug creation generates valuable IP absci. 40#41BETTER BIOLOGICS FASTER Accelerating while increasing Pos Patent Life Traditional IY DISCOVERY & PRECLINICAL 2Y 3Y 4Y ~4-6 YEARS vs. Absci ~2 YEARS ABSCI CORPORATION 2023 ALL RIGHTS RESERVED ***** POTENTIALLY $5m-7m <2 years 5Y ACCELERATING TIMELINES time to clinic 6Y 7Y < 8 YEARS Optimal candidate properties into clinic 8Y $10m-15m 4-6 years Sup-optimal candidate in clinic CLINICAL DEVELOPMENT 9Y ● 1ΟΥ ● ~8 YEARS 11Y 12Y 13Y 14Y 15Y COMMERCIALIZATION 16Y 17Y 18Y 19Y LONGER TIME ON PATENT FOR COMMERCIALIZATION ‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒ ‒‒‒‒‒‒‒‒‒‒‒‒ 20Y ACCELERATED TIME TO CLINIC CANDIDATE WITH OPTIMAL PROPERTY PROFILE INCREASES CLINICAL POS WITH CHANGE FOR SHORTER DEVELOPMENT EXPANDED PORTFOLIO WITH MORE 'SHOTS ON GOAL' POTENTIALLY LONGER TIME ON PATENT INCREASING BACK-END REVENUES absci. 4 41#42BUSINESS MODEL Creating Compounding Value for Shareholders Illustrative Economic Structure of a Successful Drug Discovery Partnership UPFRONT & TECHNOLOGY DEVELOPMENT ⠀⠀⠀ IY 2Y ABSCI ABSCI CORPORATION 2023 ALL RIGHTS RESERVED 3Y CLINICAL MILESTONES 4Y ⠀⠀ 5Y 6Y 7Y 8Y 9Y 10Y Years Since Program Start: Stage of Development: *Illustrative of Discovery Partnership; assuming successful commercialization. Regulatory milestone captured in clinical development, and single digit royalty rates 11Y 12Y DEVELOPMENT & REGULATORY COMMERCIAL MILESTONES & ROYALTIES 13Y 14Y 15Y 16Y 17Y 18Y 19Y 20Y COMMERCIALIZATION 21Y 22Y 23Y 24Y 25Y absci. 42#43Well positioned to revolutionize Al drug creation absci ABSCI CORPORATION 2023 ALL RIGHTS RESERVED inimit unan't T >160 77,000+ Square Feet ~$450M NASDAQ Listed Unlimiters with deep experience in Al, immunology, synthetic biology, and protein expression. Leading Al team with expertise from: OpenAI Meta l'lii HARVARD Google TESLA UNIVERSITY VE RO TAS State-of-the-art drug creation and wet lab space in Vancouver WA, Absci Al Research (AAIR) lab in NYC, and the Innovation Centre in Zug Switzerland Capital raised to date $ABSI IPO July 22, 2021, ten years after founding in a basement lab absci. 43#44PARTNERSHIPS Technology validated through industry-leading collaborations ◆ MERCK "Merck leans into Al with $610M in biobucks for Absci drug discovery pact" "At Merck we are continually evaluating new ways to build, expand, and refine our biologics capabilities. Absci's platform offers a compelling opportunity to design new biologic candidates and explore the expression of complex proteins."* Dr. Fiona Marshall Former SVP, Head of Discovery, Preclinical and Translational Medicine ABSCI CORPORATION 2023 ALL RIGHTS RESERVED *https://investors.absci.com/news-releases/news-release-details/absci-announces-research-collaboration-merck NVIDIA "Absci collaborates with NVIDIA, pioneer in Al & compute technology to accelerate vision of creating drugs in silico" "Absci's powerful data generation and Al protein engineering platform is already helping the drug discovery industry, and NVIDIA is excited to help power and scale Absci's in silico technologies to achieve the best positive impact." Kimberly Powell Vice President of Healthcare absci. 44#45TRAILBLAZING MANAGEMENT TEAM The right leadership team to accomplish the Impossible SEAN MCCLAIN Founder & CEO Director BAYER ANDREAS BUSCH, PHD Chief Innovation Officer BAYER Senior leadership bring experience from industry leaders including: ZACH JONASSON, PHD Chief Financial & Business Officer Google hp Pfizer ABSCI CORPORATION 2023 ALL RIGHTS RESERVED JACK GOLD Chief Marketing Officer gsk AMGEN NIKE GlaxoSmithKline KARIN WIERINCK Chief People Officer Ⓡ AMARO TAYLOR-WEINER, PHD PENELOPE SVP, Chief Al Officer Pit 103 120 HARVARD TAS UNIVERSITY RSITY UN OF Chief Morale Officer UNIVERSITY OF OXFORD Shire novo nordisk absci. 45#46Backed by a Board of industry, platform, and scientific innovators Board of Directors Scientific Advisory Board SEAN MCCLAIN Founder & CEO Director, Absci VICTOR GREIFF, PHD Associate Professor, University of Oslo ABSCI CORPORATION 2023 ALL RIGHTS RESERVED KAREN MCGINNIS, CPA Former CAO, Illumina TIM LU, MD Co-Founder and CEO, Senti Biosciences AMRIT NAGPAL Managing Director, Redmile Group HUBERT TRUEBEL, MD, PHD Head of Development, CMO, AiCuris JOSEPH SIROSH, PHD Vice President, Alexa Shopping, Amazon DAN RABINOVITSJ VP Connectivity, Meta Redmile Group Pit PHILIPS FRANS VAN HOUTEN Former CEO, Royal Phillips ∞ Meta amazon 1vat 120 HARVARD TAS UNIVERSITY illumina BAYER А BAYER absci. 46#47Absci is leading the way in Al drug creation towards breakthrough therapeutics at the click of a button ABSCI CORPORATION 2023 ALL RIGHTS RESERVED Scalable wet-lab technologies INNOVATION & VALUE CREATION 10 YEARS AGO Integrated Drug Creation™ platform 414 2 YEARS AGO Al de novo antibody design Al lead optimization TODAY Fully in silico drug creation absci. 47#48absci. This revolution is only just beginning. ABSCI CORPORATION 2023 ALL RIGHTS RESERVED absci. 48