EX-99.2 3 generalcorporatepresenta.htm EX-99.2
from absci import de_novo_model model = de_novo_model.load_latest() antigen = model.load_pdb("7olz.pdb", chain="A") antibodies = model.predict(antigen, N=300000) from absci_library import codon_optimizer library = codon_optimizer.reverse_translate(library) library.to_csv("covid-antibody-designs.csv") library.to_wet_lab(assay="ACE") from absci import lead_opt_model lead_optimizer = lead_opt_model.load_latest() library.naturalness = lead_optimizer.naturalness(library) lead_optimizer.optimize(library).to_wet_lab(as say="SPR") from absci import genetic_algorithm; parameters=["maximize|binding_affinity:pH=7.5", "minimize|binding_affinity:pH=6.0", "maximize|human_naturalness"]; library = genetic_algorithm.multiparametric_optimization(library, parameters, evolutions=100); library.to_wet_lab(assays=["ACE", "SPR", "Bioassays"]) C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . CORPORATE PRESENTATION SUMMER 2024

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 2 Disclaimers Forward-Looking Statements Certain statements in this presentation that are not historical facts are considered forward-looking within the meaning of the Private Securities Litigation Reform Act of 1995, 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, estimated speed, cost advantages, improved success rates, and expanded intellectual property opportunities from developing therapeutics leveraging our AI drug creation platform, potential milestone and royalty payments due under our collaboration agreements, projected costs, prospects, plans and objectives of management, our technology development efforts and the application of those efforts, including for generalizing our platform, accelerating drug discovery and development timelines, increasing probability of successful drug development and developing better product candidates, our drug discovery and development activities related to drug creation partnerships and our internal therapeutic asset programs, the progress, milestones and success of our internal asset programs, including ABS-101, including our clinical development strategy, the progress and timing for various stages of development including candidate selection, IND enabling studies, initiating clinical trials and the generation and disclosure of data related to these programs, the translation of preclinical results and data into product candidates, and the significance of preclinical results, including in comparison to competitor molecules for ABS-101 and in leading to differentiated clinical efficacy or product profiles, 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 our ability to secure milestone payments and royalties, obtaining and maintaining necessary approvals from the FDA and other regulatory authorities, replicating in clinical trials positive results observed in preclinical studies, our dependence on third parties to support our internal asset programs, including for the manufacture and supply of preclinical and clinical supplies of our product candidates or components thereof, our ability to effectively collaborate on research, drug discovery and development activities with our partners or potential partners, our existing and potential partners’ ability and willingness to pursue the development and commercialization of programs or product candidates under the terms of our partnership agreements, and overall market conditions and regulatory developments that may affect our and our partners’ activities under these agreements; 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®, the Absci logo mark ( ), SoluPro®, Bionic 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 AI logo mark ( ), the Unlimit with us mark ( ), the unlimit symbol ( ), Bionic protein ™, Bionic Enzyme ™, Bionic Antibody™, 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 intellectual 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.

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . Absci is a Data-First Generative AI Drug Creation Company Our Integrated AI & Wet Lab Platform Aims to Engineer Better Biologics Faster • Ultra-Efficient Discovery • Best-in-Class Properties • Access Difficult Targets • Unlock Novel Biology P L A T F O R M V A L I D A T E D T H R O U G H I N D U S T R Y - L E A D I N G P A R T N E R S H I P S I N C L U D I N G W I T H A S T R A Z E N E C A , M E R C K A N D N V I D I A D I F F E R E N T I A T E D L A B - I N - A - L O O P : ‘ D A T A T O T R A I N ’ , ‘ A I T O C R E A T E ’ , & ‘ W E T L A B T O V A L I D A T E ’ I N R A P I D 6 - W E E K C Y C L E S I N T E R N A L P I P E L I N E O F P O T E N T I A L L Y ‘ B E S T - I N - C L A S S ’ & ‘ F I R S T - I N - C L A S S ’ A S S E T P R O G R A M S F O C U S E D O N C Y T O K I N E B I O L O G Y L E A D A S S E T A B S - 1 0 1 , A D I F F E R E N T I A T E D T L 1 A A N T I B O D Y D E S I G N E D U S I N G A B S C I ’ S D E N O V O A I A D V A N C I N G T O W A R D S C L I N I C I N E A R L Y 2 0 2 5

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 4 L O N G I T E R A T I V E P R O C E S S R E S U L T I N G I N D R U G C A N D I D A T E S W I T H S U B O P T I M A L A T T R I B U T E S L I M I T E D C O N T R O L O F A T T R I B U T E S O F T H E R A P E U T I C S N O A B I L I T Y T O S E L E C T E P I T O P E 5 . 5 Y E A R S F R O M D I S C O V E R Y T O I N D OPTIMIZE FOR AFFINITY OPTIMIZE FOR TOXICITY OPTIMIZE FOR DEVELOPABILITY SUBOPTIMAL CANDIDATE < 5 % S U C C E S S R A T E F R O M D I S C O V E R Y T O L A U N C H The Drug Discovery Paradigm is Ripe for Disruption T H E P R O B L E M — C U R R E N T N E E D F O R G E N E R A T I V E A I

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 5 W H Y H A S N ’ T G E N E R A T I V E A I T R A N S F O R M E D B I O L O G I C D R U G D I S C O V E R Y ? Unlocking the Potential of Generative AI in Biology Requires Scalable Biological Data S M A L L M O L E C U L E B I O L O G I Cv. Extensive Libraries Limited Public Data and technologies to scale data Consistent and accurate data is limited B I O L O G I C S R E Q U I R E L I V I N G O R G A N I S M S T O P R O D U C E D R U G V A R I A N T S F O R T E S T I N G U N L O C K I N G T H E P O T E N T I A L O F G E N E R A T I V E A I I N B I O L O G Y … …requires generating scalable biological data WET LAB AI

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . T H E S O L U T I O N Absci’s E. coli SoluPro cell line generates billions of cells, expressing proteins of interest Absci’s ACE Assay™ technology generates data at >4,000x the throughput of traditional HT assays Massive and Growing Training Data Sets S O L U P R O ™ C E L L L I N E A C E A S S A Y ™ Billions of cells, expressing proteins of interest Millions of antibody sequence variants + billions of parameters in weeks P R O P R I E T A R Y A S S A Y S P U B L I C D A T A S E T S Absci is Solving the Problem of Scalable Biological Data to Enable True Generative AI for Biology

7C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . A I T O C R E A T E Advanced generative AI models used to create antibodies and next-gen biologics through de novo design and optimization • De novo antibody creation is prompted with antigen structure, epitope location, and framework sequences and returns designed CDRs • Proprietary generative AI models use architectural innovations to access a massive sequence search space, up to ~20^55, to design antibody-antigen complex structures and sequences in silico Integrated Drug Creation™ Platform: Lab-in-a-Loop + Proprietary Data + Advanced Generative AI Models D A T A T O T R A I N Wet lab assays generate massive quantities of high-quality data for generative AI model training • ACE Assay™ measures binding affinity and target specificity of millions of antibody sequences in a single week. • ACE Assay™ data is combined with additional proprietary generated data and public data sets. Polyreactivity Solubility W E T L A B T O V A L I D A T E 77,000 sq ft+ lab to validate AI-generated designs Thermostability Potency FcRn recycling • Assess binding affinity and target specificity for up to 3 million of ranked antibody sequences from billions of AI-designed antibodies. • Lower throughput assays confirm other predicted properties for lead designs: Self-association Hydrophobicity 6 - W E E K I T E R A T I V E C Y C L E S C O N T I N U A L L Y I M P R O V E S G E N A I M O D E L S Resistance to stress

8C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . D A T A T O T R A I N G E N E R A T I V E A I M O D E L S W E T L A B T O V A L I D A T E D E S I R E D F U N C T I O N A N D P R O P E R T I E S A I T O C R E A T E N O V E L B I O T H E R A P E U T I C D E S I G N S Partner nominated targets E N G I N E E R - I N O P T I M A L A T T R I B U T E S O F T H E R A P E U T I C A N T I B O D I E S o EPITOPE SPECIFICITY o OPTIMIZE EPITOPE INTERACTION § DESIRED MOA o ENHANCED POTENCY o ENHANCED DEVELOPABILITY o DIFFERENTIATED FEATURES Reverse Immunology target discovery Absci’s Integrated Drug Creation™ Platform to Engineer Optimal Drug Attributes

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 9 Absci is the first to design and validate novel antibodies* using zero-shot generative AI Used artificial intelligence to simultaneously optimize multiple parameters important to drug discovery and development (Bachas et al. 2022) in vitro validated antibody design against multiple therapeutic antigens using generative inverse folding model (Shanehsazzadeh et al. 2023) D E C 2 0 2 3 A U G 2 0 2 2 Functional wet-lab validation of novel antibodies designed using zero-shot generative AI - demonstrating the potential to go from target to therapeutic antibody at a click of a button (Shanehsazzadeh et al. 2024) * M A R 2 0 2 3 - U P D A T E D J A N 2 0 2 4

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 10 Leveraging Generative AI Capabilities to Access Novel Biology and Rapidly Design Therapeutics with Best-in-Class Properties . . D E N O V O A I F O U N D A T I O N M O D E L A I L E A D O P T I M I Z A T I O N M O D E L A I D E S I G N E D F E A T U R E S ü Epitope specificity ü Global epitope landscaping to identify epitopes with desired MoA ü Local epitope landscaping to identify desired epitope interactions for potentially improved potency and MoA ü Local epitope interface evolution to improve desired epitope interactions for potentially improved potency and desired MoA ü Multi-parametric developability optimization Novel Features: ü pH depending binding ü Half-life extension ü Multi-valency / multiple targets Design of therapeutic antibodies to novel and challenging targets § Novel targets including GPCRs and ion channels Rapid design of fast follower therapeutic antibodies to validated targets § 12-14 months to Drug Candidate § Best-in-class Potential

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 11 Integrated Drug CreationTM Platform Leveraging AI Throughout the End-to-End Drug Discovery Process T A R G E T D I S C O V E R Y W I T H N O V E L A P P R O A C H E S Reverse Immunology for target discovery A I - G U I D E D L E A D O P T I M I Z A T I O N Multi-parameteric optimized antibodies A I - G U I D E D A N T I B O D Y D R U G C R E A T I O N De novo antibodies designed by AI

12C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . Generative AI Drug Creation™ workflow TARGET OF INTEREST De novo AI Pre-Clinical Development FUNCTION CONFIRMED CandidateLead OPTIMIZED FOR: Affinity, specificity, developability…. Absci works with its partners to set the goals of partnership programs: (1) Target antigen (2) Target antigen epitope(s) if known OR global epitope landscaping to identify optimal epitope / MOA (3) Drug modality, e.g. mAb, bi-specific, fusion protein (4) Target TPP parameters FUNCTION VALIDATED Multi-parameter AI Optimization

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 13 Platform Enables the Potential to Deliver Differentiated Biologics, Faster at Lower Cost V A L U E D R I V E R S ENABLING FIRST-IN-CLASS A C C E S S N O V E L D I S E A S E B I O L O G Y Ability to address elusive drug targets, e.g. GPCRs, Ion Channels ENABLING BEST IN CLASS & HIGHER PROGRAM NPVS I N C R E A S E D P R O B A B I L I T Y O F S U C C E S S Superior Drug Attributes and Multidimensional optimization creates higher quality biologics ENHANCED IP PROTECTION E X P A N D E D I N T E L L E C T U A L P R O P E R T Y S P A C E Generates broader IP for First-in-Class therapies and finds new IP for Best-in-Class therapies FASTER TIME TO IND R E D U C E D T I M E & C O S T T O C L I N I C 2 years and $14-16M from Target to IND; significant reduction compared to industry estimates

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 14 Internal Pipeline of Potential First-in-Class and Best-in-Class Assets P I P E L I N E H I G H L I G H T S POTENTIAL “BEST-IN-CLASS” POTENTIAL “FIRST-IN-CLASS” DERMATOLOGY / UNDISCLOSED INFL. BOWEL DISEASE / TL1A IMMUNO-ONCOLOGY / UNDISCLOSED A B S - 2 0 1 A B S - 3 0 1 A B S - 1 0 1 T A R G E T V A L L E A D C A N D I D A T E I N D - E N A B L I N G T H E R A P E U T I C A R E A / T A R G E T IND Focus on cytokine biology - first frontier of AI-driven disruption ADDITIONAL EARLY DISCOVERY PROGRAMS

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 15 Inflammation Fibrosis Clinically Validated Mechanism of Action in Large Underserved Market A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S T L 1 A : D R 3 S I G N A L I N G C L I N I C A L L Y S H O W N T O I N D U C E P R O - I N F L A M M A T O R Y R E S P O N S E S 1 P O T E N T I A L R E L E V A N C E I N W I D E R A N G E O F A U T O I M M U N E I N D I C A T I O N S DC MΦ Membrane TL1ADR3 T-Cell Signaling Signaling DR3 Soluble TL1A 2 Wang 2023 http://dx.doi.org/10.1136/bmjopen-2022-065186 3 Dahlhamer, James M., et al. "Prevalence of inflammatory bowel disease among adults aged≥ 18 years—United States, 2015." Morbidity and mortality weekly report 65.42 (2016): 1166-1169. 4 Evaluate Pharma Oct 2023. 1 Adapted from Takedatsu 2008 doi: 10.1053/j.gastro.2008.04.037 Significant market opportunities beyond Inflammatory Bowel Disease $22B+ Global Market4 $4.5B for TL1A 0.8- 3M U.S. Inflammatory Bowel Disease Prevalence3 5M Global Inflammatory Bowel Disease (IBD) Prevalence2

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 16 Potential Best-in-Class TL1A mAb Designed using Generative AI A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S AI-designed TL1A program designed to achieve superior therapeutic properties over clinical competitors D E N O V O A I - D E S I G N E D A N D A I - O P T I M I Z E D S U P E R I O R P R E - C L I N I C A L P R O F I L E A N D P O T E N T I A L F O R S U P E R I O R C L I N I C A L P R O F I L E D I F F E R E N T I A T E D I N T E L L E C T U A L P R O P E R T Y • High Affinity & Potency • High affinity to both the TL1A trimer and monomers • Extended Half-life & Longer Dosing Intervals • Q8W to once quarterly • Low immunogenicity • Sub-Q Dosing • High bioavailability • Favorable Developability • Target to promising candidates in just over 1 year

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 17 AI Platform Designed Leads Span Diverse Set of Epitopes Leading to IP Differentiation and Superior Preclinical Profile A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S * Epitope binning by BLI competition experiment # Estimated performance of clinical competitor reagent generated for comparison De novo model performed local epitope landscaping Absci selected hypothesized immuno- privileged epitope for de novo model. Epitope also selected to enable both TL1A monomer and trimer binding AI Lead Optimization model performed further local epitope evolution 3 lead candidates identified with novel epitope interactions à improved affinity and potency MK-7240 # RVT-3101 # Epitope bins on TL1A* ABS-101-A ABS-101-B ABS-101-C

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 18 TL1A Target identified Function confirmed Multi-parametrically optimized Functionally validated D E N O V O A I A I L E A D - O P T I M I Z A T I O N Hits Lead Candidate Potential Best-in-Class TL1A mAb Designed using Generative AI A B S - 1 0 1 T L 1 A

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 19 A ve ra ge IC 50 ( nM ) AI Platform Designed Advanced Leads with High Affinity and Superior Potency A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S H I G H A F F I N I T Y m A B s W I T H P R E S E R V E D C R O S S - R E A C T I V I T Y A I - O P T I M I Z E D L O W p M A F F I N I T Y T R A N S L A T E S T O S U P E R I O R O R E Q U I V A L E N T P O T E N C Y #Estimated performance of a putative clinical competitor molecule generated for in house comparison. In cr ea si ng a ff in ity In cr ea si ng p ot en cy ABS-10 1-A (D C) ABS-10 1-B ABS-10 1-C RVT-3 10 1# MK-7 240 # AFFINITY BY SURFACE PLASMON RESONANCE (SPR) APOPTOSIS INHIBITION ASSAY IN TF-1 CELLS # # # Increasing affinity (DC)

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 20 AI Epitope Selection Enables High Affinity to Both the TL1A Monomer and Trimer E P I T O P E S E L E C T E D E N A B L E D H I G H A F F I N I T Y B I N D I N G T O B O T H T H E T L 1 A M O N O M E R A N D T R I M E R #Estimated performance of clinical competitor reagent generated for in-house comparison. 1 We used BLI values for comparing monomer and trimer binding and not as absolute values due to sensitivity limits for the instrument at high affinity. SPR-based absolute affinities reported in the previous slide are considered more accurate. For samples, such as RVT-3101#, the observed difference in affinities measured by SPR and BLI are within the error expected for picomolar binders by BLI. A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S In cr ea si ng a ff in ity Increasing affinity RVT-3101 MK-7240 TEV-48574 ABS-101-C ABS-101-A ABS-101-B # # # (DC)

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 21 Favorable in vitro Profile and PK Profile for Longer Dosing Intervals Extended half-life in vitro compared to competitors# Increased recycling in in vitro FcRn Assay1 PK data in Tg32 mice show lead candidates with extended half-life in vivo relative to RVT-3101# and TEV-48574# PK Parameters ABS-101-A ABS-101-C RVT-3101# TEV-48574# t1/2 (d) 12 14 9 5 CL (mL/hr/kg) 0.61 0.39 0.52 3.26 AUC0-∞ (µg.d/mL) 688 1060 805 128 Vss (mL/kg) 198 148 121 197 1 Cell-based FcRn recycling assay in HMEC-1 cells. Grevys 2018 2 Homozygous hFcRn Tg32 mouse model, single dose i.v. #Estimated performance of a putative clinical competitor molecule generated for in house comparison Increasing recycling Re cy cl ed m A b co nc en tr at io n (n M ) ABS-10 1-A (D C) RVT-3 10 1# 10 20 0 ABS-10 1-C 30 40 TEV-4 9574# Herc eptin Tra st uzum ab A A Tra st uzum ab LS A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S (DC)

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 22 A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S Extended half-life of 2-3-fold over clinical competitors to support Q8W-Q12W dosing interval. ABS-101 shows enhanced biodistribution in NHPs, compared to antibodies in clinical development. Potential therapeutic advantage due to faster tissue penetration, likely without the need for a loading dose. Successful development of high-concentration drug substance formulation at 200mg/mL to enable subcutaneous injection. 2-3x Extended Half-life in Non-Human Primates (NHPs) Compared to Clinical Competitors 2 - 3 X E X T E N D E D H A L F - L I F E I N N H P s S U P P O R T S P O T E N T I A L L O N G E R D O S I N G I N T E R V A L S H I G H C O N C E N T R A T I O N F O R M U L A T I O N E N A B L E S S U B C U T A N E O U S I N J E C T I O N 2 - 3 X L O N G E R H A L F - L I F E I N N H P s C O M P A R E D T O C L I N I C A L C O M P E T I T O R S m A b se ru m c on c (µ g/ m l) Time (days) 0 10 20 30 40 50 60 10¹ 10² 10³ ABS-101-A MK-7240 RVT-3101

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 23 AI Platform Designed ABS-101 Aims for Optimal Therapeutic Profile A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S A T T R I B U T E A B S - 1 0 1 P R O G R A M * M E R C K ( P R O M E T H E U S ) M K - 7 2 4 0 R O C H E ( R O I V A N T ) R V T - 3 1 0 1 S A N O F I ( T E V A ) T E V - 4 8 5 7 4 High affinity/potency ü ü ü Monomer and trimer TL1A binding ü ü Low Immunogenicity** ü ü1 1, 3 - High Bioavailability ü ü1 1, 4 - Sub-Q injection ü ü5 ü6 7 Q8W to once quarterly dosing ü 1, 2 1, 2 8 *ABS-101 parameters projected from in silico, in vitro, and in vivo (NHP) metrics and modeled exposure with ½-life extension. ** Low score by in silico immunogenicity metrics and low results in ex vivo T-cell assay. 1 Based on Phase 2 data 2 Once monthly dosing regimen 3 82% of Ph2a participants developed ADA, likely due to formation of large immune complexes. Danese et al. 2021 https://doi.org/10.1016/j.cgh.2021.06.011 4 45% BA at 100 mg/mL based on Ph2 data 5 High dose intravenous dose, followed by high dose subcutaneous administration, based on Phase 3 protocol. Unknown if injection or infusion. NCT06052059, NCT06430801 6 Expected commercial form factor 7 Administered by subcutaneous infusion, not injection, based on Phase 2 protocol, NCT05499130, NCT05668013 8 Based on Phase 2b protocol, NCT05668013

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 24 A B S - 1 0 1 T L 1 A D A T A H I G H L I G H T S Projected Timeline to Potential Best-in-Class Molecule AI-Designed Advanced Leads have Demonstrated: ü High Affinity ü High Potency ü Long Half-Life ü Favorable Manufacturability J A N 2 0 2 4 I N I T I A T E D F E B ‘ 2 4 1 H 2 5 IND-enabling studies to evaluate ü Development candidate selected Feb ‘24 ü Sub-Q formulation ü Favorable PK and long Hal-Life ü High Bioavailability in NHPs • Low ADA • High Tolerability (low tox) Initiating Phase 1 Trial 2 H 2 5 Phase 1 Interim Data Readout

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 25 Over $900M + Royalties of Deal Value in H2 2023 R E C E N T P A R T N E R S H I P S “This collaboration is an exciting opportunity to utilize Absci's de novo AI antibody creation platform to design a potential new antibody therapy in oncology." D R . P U J A S A P R A AstraZeneca, SVP, Biologics Engineering & Oncology Targeted Delivery “Almirall chose Absci because their de novo platform brings truly novel innovation in solving the industry’s most challenging targets facing high unmet medical need.” Almirall, Chief Scientific Officer and EVP of Research & Development D R . K A R L Z I E G E L B A U E R R&D CLINICAL COMMERCIAL ROYALTIES I L L U S T R A T I V E E C O N O M I C S T R U C T U R E O F A S U C C E S S F U L D R U G D I S C O V E R Y P A R T N E R S H I P UPFRONT & RESEARCH FUNDING CLINICAL MILESTONES COMMERCIAL MILESTONES & ROYALTIES

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 26 P A R T N E R S H I P S A I D R U G D I S C O V E R Y C O L L A B O R A T I O N S I N T E R N A L A S S E T P I P E L I N E C O L L A B O R A T I O N S D A T A & C O M P U T E C O L L A B O R A T I O N S Trademarks, service marks or trade names referred to herein are the intellectual property of their respective owners. Use of this IP does not imply affiliation, endorsement or sponsorship of any kind 3 Internal Programs with Potential Best-in -Class or First- in -Class Profile Driving Growth Through Industry-Leading Collaborations

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 27 Leadership Team of Innovators Across AI and Biotech to Transform Drug Discovery W O R L D C L A S S T E A M Trademarks, service marks or trade names referred to herein are the intellectual property of their respective owners. Use of this IP does not imply affiliation, endorsement or sponsorship of any kind KAREN MCGINNIS, CPA Former CAO, Illumina AMRIT NAGPAL Managing Director, Redmile Group JOSEPH SIROSH, PHD Former CTO, Compass VP, Amazon & Microsoft DAN RABINOVITSJ Vice President Connectivity, Meta FRANS VAN HOUTEN Chairman of the Board Former CEO, Royal Phillips B O A R D O F D I R E C T O R S SIR MENE PANGALOS, PHD Former EVP R&D AstraZeneca L E A D E R S H I P T E A M SEAN MCCLAIN Founder, CEO & Director ANDREAS BUSCH, PHD Chief Innovation Officer ZACH JONASSON, PHD Chief Financial Officer & Chief Business Officer AMARO TAYLOR-WEINER, PHD SVP, Chief AI Officer KARIN WIERINCK Chief People Officer CHRISTIAN STEGMANN, PHD SVP, Drug Creation CHRISTINE LEMKE, DVM SVP, Portfolio & Growth Strategy JENS PLASSMEIER, PHD SVP, Biologics Discovery Technologies PENELOPE Chief Morale Officer SHELBY WALKER, JD Chief Legal Officer

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 28 >160 77,000+ Square Feet >$520M Unlimiters with deep experience in AI, drug discovery, immunology, and synthetic biology State-of-the-art drug creation and wet lab space in Vancouver WA, Absci AI Research (AAIR) lab in NYC, and the Innovation Centre in Zug Switzerland Capital raised to date Biologics drug discovery expertise from: Leading AI team with expertise from: Absci’s Talent and Infrastructure for Better Biologics Faster, at Lower Cost Trademarks, service marks or trade names referred to herein are the intellectual property of their respective owners. Use of this IP does not imply affiliation, endorsement or sponsorship of any kind W E L L - P O S I T I O N E D T O D E L I V E R

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 29 Integrated Drug CreationTM Platform Leveraging AI Throughout the End-to-End Drug Discovery Process T A R G E T D I S C O V E R Y W I T H N O V E L A P P R O A C H E S Reverse Immunology for target discovery A I - G U I D E D L E A D O P T I M I Z A T I O N Multi-parameteric optimized antibodies A I - G U I D E D A N T I B O D Y D R U G C R E A T I O N De novo antibodies designed by AI

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 30 de novo Designed Antibodies de novo antibody design using generative AI T A R G E T D I S C O V E R Y W I T H N O V E L A P P R O A C H E S Reverse Immunology for target discovery A I - G U I D E D L E A D O P T I M I Z A T I O N Multi-parameteric optimized antibodies A I - G U I D E D A N T I B O D Y D R U G C R E A T I O N De novo antibodies designed by AI

31C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . Generative AI Drug Creation™ Workflow TARGET OF INTEREST De novo AI Pre-Clinical Development FUNCTION CONFIRMED CandidateLead OPTIMIZED FOR: Affinity, specificity, developability, etc. Absci works with its partners to set the goals of partnership programs: (1) Target antigen (2) Target antigen epitope(s) if known OR global epitope landscaping to identify optimal epitope / MOA (3) Drug modality, e.g. mAb, bi-specific, fusion protein (4) Target TPP parameters FUNCTION VALIDATED Multi-parameter AI Optimization

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 32 Antibody scaffold sequence De novo antibody designs Wet lab assays ACE Assay™ data SPR assay data Target antigen structure Validated de novo binders Target epitope Antibody scaffold sequence De novo drug creation with ‘zero-shot’ generative AI Zero-Shot: Model has never seen an antibody that binds to the target or homologs Binders were identified straight out of the model – no lead optimization was performed

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 33 Example: de novo design of HER2 antibodies Demonstration of ‘zero shot’ model by designing HCDR3 and HCDR123 for HER2 Assessed multiple parameters: • Binding rates • Sequence diversity • Immunogenicity • Functionality • Developability P O C D E M O N S T R A T E D 1. De novo models generated diverse, novel, and high affinity variants superior to baseline 2. Demonstrated high level of specificity 3. Demonstrated higher potency vs Trastuzumab in vitro 4. Achieved multi-dimensional lead optimization • Desired cross-species reactivity and specificity • Optimal developability P O C M O D E L D E N O V O D E S I G N

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 34 AI Generated Diverse, Novel & High Affinity Binders that Outperforms Biological Baseline D E N O V O D E S I G N O F H E R 2 A N T I B O D I E S SIZE OF SEARCH SPACE: MILLIONS BILLIONS TRILLIONS QUADRILLIONS 9.0 8.5 8.0 7.5 7.0 6.5 6.0 2 3 4 5 6 7 8 9 10 11 12 M EA SU RE D B IN D IN G A FF IN IT Y (- lo g 1 0K D (M )) SEQUENCE DIVERSITY (EDIT DISTANCE TO TRASTUZUMAB) AI-design Wildtype Diverse, novel, high affinity binders • Up to 12 mutations in a CDR region of 13 amino-acids (Search space of 2013) Outperforms biological baseline • De novo designed HCDR3s achieve a 4-fold improvement over random OAS baseline Affinity of novel binders up to 3.4 nM measured by SPR in mAb format HER2 BINDING RATE % Random baseline Biological baseline Zero-shot de novo generated designs 1

35C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . G L O B A L E P I T O P E L A N D S C A P I N G L O C A L I N T E R F A C E E V O L U T I O N Epitope landscaping and interface evolution can be used to improve affinity, potency and to potentially uncover novel Mechanisms of Action (MoAs) De novo AI model allows sampling multiple epitope interfaces across the antigen to locate desired MoA L O C A L E P I T O P E L A N D S C A P I N G Once an epitope is selected the de novo model exhaustively samples the interface contacts with the designated epitope to further refine potency and MoA De novo and Lead Optimization AI models further enable global and local epitope landscaping In addition to optimizing antibody variants for developability, the AI lead optimization model samples the epitope interface with its surrounding adjacent region to further improve potency and MoA Epitope(s) of interest Epitope of interest Epitope + adjacent region de novo AI model de novo AI model AI lead optimization model

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 36 AI-Guided Lead Optimization From de novo design to multiparametric lead optimization using AI T A R G E T D I S C O V E R Y W I T H N O V E L A P P R O A C H E S Reverse Immunology for target discovery A I - G U I D E D L E A D O P T I M I Z A T I O N Multi-parameteric optimized antibodies A I - G U I D E D A N T I B O D Y D R U G C R E A T I O N De novo antibodies designed by AI

37C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . Generative AI Drug Creation™ Workflow TARGET OF INTEREST De novo AI Pre-Clinical Development FUNCTION CONFIRMED CandidateLead OPTIMIZED FOR: Affinity, specificity, developability, etc. Absci works with its partners to set the goals of partnership programs: (1) Target antigen (2) Target antigen epitope(s) if known OR global epitope landscaping to identify optimal epitope / MOA (3) Drug modality, e.g. mAb, bi-specific, fusion protein (4) Target TPP parameters FUNCTION VALIDATED Multi-parameter AI Optimization

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 38 Re-engineer clinically approved antibody for binding towards three SARS-CoV-2 variants Improve binding towards beta without loss of binding towards alpha and delta alpha N501Y N501Y beta L452R E484QE484K K417N/T delta SARS-CoV-2 variant mutations in the receptor binding domain (RBD) SARS-CoV-2 RBD Epitope residues Strain mutations Fab KD (nM) WT RBD alpha RBD beta RBD delta RBD Parental Antibody 8.5 8.0 607 5.4 Case study goals AI multi-valent co-optimization of a broad-spectrum SARS-CoV-2 antibody A I - G U I D E D L E A D O P T I M I Z A T I O N

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . Model training Hold out data sets demonstrate strong model performance following training with AI-predicted affinity correlating well with experimental measurements >75k ACE Assay™ data points per training set Representative holdout set (beta) alpha beta delta 0.75 0.841 0.78 Pearson R Parent Antibody A I- pr ed ic te d A C E A ss ay Sc or e™ (a ff in ity ) Wet lab ACE Assay Score™ (affinity) 1 2 1 High correlation between ACE Score™ and SPR-measured -log10 KD values observed Absci’s ACE Assay™ Platform Generates Large, High Quality Training Data Enabling in silico Affinity Predictions A I - G U I D E D L E A D O P T I M I Z A T I O N

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 40 SPR measurements 0.10.1 0.1 3 Binders predicted to have the best binding towards all three SARS-CoV-2 variants are assessed in the lab by SPR 79% (31/39) of evaluated predictions exhibit higher binding affinity than parent antibody to alpha and beta and delta AI Model Searches Mutational Space and Top Predictions are Validated A I - G U I D E D L E A D O P T I M I Z A T I O N

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 41 SPR KD A C E A ss ay Sc or e™ Large quality data sets Model training Model predictions SPR validation Predicted 1 Pr ed ic te d 2 Fab nM KD (fold improvement) alpha RBD beta RBD delta RBD Parental antibody 8.0 607 5.4 ABSCI001 2.7 (3x) 16 (37x) 1.9 (3x) ABSCI002 1.5 (5x) 24 (25x) 0.8 (7x) ABSCI003 0.9 (9x) 32 (19x) 0.6 (9x) ABSCI004 1.1 (7x) 37 (16x) 1.4 (4x) ABSCI005 1.3 (6x) 40 (15x) 0.8 (7x) AI-guided lead optimization platform delivers antibodies with improved binding towards all three desired variants Case study outcome AI co-optimized binding to multiple SARS-CoV-2 variants A I - G U I D E D L E A D O P T I M I Z A T I O N

42C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . Half-life extension Epitope selection DE NOVO DESIGN & AI-GUIDED LEAD OPTIMIZATION FOR IMPROVED THERAPEUTIC FUNCTIONALITIES • Extend half-life through augmenting Fc-mediated recycling • Reduces dosing intervals and lowers risk of Cmax driven adverse events • Improves pharmacokinetic profile Multi-valency • Increased efficacy by simultaneous binding to multiple desired isoforms • Broad spectrum antibodies with simultaneous binding to multiple viral variants for infectious diseases • Cross-species binding for improved success rates and speed Novel AI-designed functionalities • Global landscaping assess multiple epitopes of interest for the desired functionality • Local landscaping evaluates a diverse set of interfaces of a specific epitope • Interface refinement with lead optimization models for improved potency and / or developability Epitope(s) of interest A I - G U I D E D L E A D O P T I M I Z A T I O N

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 43 Target Discovery Reverse Immunology platform unifies target and antibody discovery in a single workflow enabling potential “first-in-class” biotherapeutics T A R G E T D I S C O V E R Y W I T H N O V E L A P P R O A C H E S Reverse Immunology for target discovery A I - G U I D E D L E A D O P T I M I Z A T I O N Multi-parameteric optimized antibodies A I - G U I D E D A N T I B O D Y D R U G C R E A T I O N De novo antibodies designed by AI

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 44 Reverse Immunology: Target and Antibody Discovery Simultaneously T A R G E T D I S C O V E R Y Pre-Clinical Development Affinity, specificity MoA ASSAYS INDICATION OF INTEREST DRUG CANDIDATE FUNCTION VALIDATED BINDING CONFIRMED Absci partners with leading health institutions for patient samples: § Aster Insights § Avera Health § Saint John’s Cancer Institute § University of Oxford, Kennedy Institute of Rheumatology Assemble antibodies and de-orphan COLLECT SAMPLES LEAD

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . The presence of TLS is associated with longer progression-free survival and better response to immune checkpoint inhibitors [2,3]. • Rapidly growing evidence illustrates correlation between TLS- derived antibodies in the tumor microenvironment and positive clinical outcomes [2]. • TLS-derived antibodies have been shown to be associated with apoptosis of cancer cells in patients [2]. TLS are centers of immune activity (B-cell proliferation and antibody production) that develop in chronically inflamed tissues [1]. Antibodies from TLS are specialized for local antigens and play a significant role in the progression of chronic diseases and cancer, setting them apart from the general population of antibodies in the peripheral blood [2]. 100 75 50 25 10 15 20 0 0 5 High Ig Staining Low Ig Staining Pr og re ss io n- fr ee ( % ) Time (months) P= 0.019 Tertiary lymphoid structures (TLS): the cornerstone of Absci’s Reverse Immunology approach T A R G E T D I S C O V E R Y [1] Pipi et al. "Tertiary lymphoid structures: autoimmunity goes local." Frontiers in immunology (2018) [2] Meylan et al. "Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer." Immunity (2022) [3] Helmink et al. "B cells and tertiary lymphoid structures promote immunotherapy response." Nature (2020)

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 46 Computationally reconstructed antibodies from human TLS biopsies High-throughput proteomics screening technology covering the human proteome close to completeness A B S - 3 0 1 : R E C O N S T R U C T E D P A T I E N T - D E R I V E D A N T I B O D Y S H O W S H I G H L Y S P E C I F I C A N D P O T E N T B I N D I N G T O A N O V E L T A R G E T W I T H P O T E N T I A L I N I M M U N O - O N C O L O G Y . ABS-301 KD: 26.5nM Biolayer interferometry validates potent and specific binding against a novel, undisclosed target Identification of a Novel Immunomodulatory Antibody ABS-301 T A R G E T D I S C O V E R Y : A B S - 3 0 1

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 47 ABS-301 blocks a novel immunosuppressive target in human cells ABS-301: Patient-derived Antibody Blocks a Novel Immunosuppressive Target T A R G E T D I S C O V E R Y : A B S - 3 0 1 Hypothesis: Tumors upregulate ABS-301’s target as an immune evasion strategy to limit immune infiltration. ABS-301 treatment in cancer may release immune suppression and permit immune cells to infiltrate the tumor, allowing for a robust anti-tumor response. Preliminary evidence suggests that this immune escape mechanism might be independent of known immune checkpoints such as the PD1/PD-L1 axis. ABS-301 Isotype control Isotype control Re ce pt or s ig na llin g (A bs . 6 20 m M , A U ) Concentration (M)

C O P Y R I G H T © 2 0 2 4 A B S C I C O R P O R A T I O N . A L L R I G H T S R E S E R V E D . 48 ABS-301 has Broad Potential in Immuno-oncology T A R G E T D I S C O V E R Y : A B S - 3 0 1 1. Siegel et al, CA, 2023, 73 (1), 17-48 2. Evaluate Pharma 3. Baxter et al, Br J Cancer 125, 1068–1079 (2021) 4. Lim, S.Y. et al, Nat Commun 14, 1516 (2023) 5. Zhou S et al, Front Immunol., 2023, 14:1129465 6. Huang Y et al, Cancers (Basel), 2023, 15(10):2733 7. Oualla K et al, Cancer Control, 2021, 10732748211004878 Indication US Estimated New Cases in 2023 [1] Estimated Global Therapeutics Market (2028)[2] NSCLC 238K $56B Melanoma 98K $14B Head & Neck 54K $5B Gastroesophageal 48K $3B 0 20 40 60 80 100 Gastroesophageal Head & Neck Melanoma NSCLC Percent Estimated ICI-Resistance Percentage Rates Among Select Oncology Indications[3-7] Comprehensive profiling of ABS-301’s immuno- oncological potential in progress.

This revolution is only just beginning. 49ABSCI CORPORATION 2024 ALL RIGHTS RESERVED