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Tuesday, 18 Nov, 2025Pre-Event Workshop
Kickstart your AIDDD experience with our pre-event workshops, designed to provide in-depth insights and hands-on learning in key areas of the industry. Join QIAGEN for a deep-dive workshop exploring its AI-powered biomedical knowledge base, designed to accelerate target identification, pathway analysis, and data-driven drug discovery through advanced omics integration.
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Wednesday, 19 Nov, 2025RegistrationMorning Panels
Ashwini Ghogare
Chief Executive Officer & Head, AI & Automation in Drug DiscoveryMilliporeSigma
David Hallett
Chief Scientific OfficerRecursion
Morten Sogaard
Senior Vice President & Head, Astellas Innovation LabAstellas Pharma
Shah Nawaz
Vice President & Chief Technology OfficerRegeneronMorning BreakMorning SessionsDiscuss how AI is used to identify pathological features, discover drug targets, and decode complex disease biology at a systems level.
Ari Allyn-Feuer
Director, AI Intelligence ProductGSK
Arvind Rao
Associate Professor, Computational Medicine & BioinformaticsUniversity of MichiganLearn how GenAI is transforming early drug discovery by designing novel, drug-like small molecules with improved potency, selectivity, and ADME properties.
Explore how GenAI integrates with synthesis planning and automation tools to prioritize viable candidates and accelerate iterative drug development.
Yue-Wang Webster
Vice President, Model Driven Drug Discovery PlatformsEli LillyLearn how predictive simulations, generative AI and differentiating clinical biomarkers are forecasted to cut prototyping timelines by weeks and reduce per‑trial costs.
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Gregory Goldmacher
Assistant Vice President, Clinical Research, Head, ImagingMerck
Pavan Choksi
PartnerArkitek VenturesExplore how ML-enabled real-time control systems and continuous process verification improve yield predictability, reduce rework, and enable faster release - offering a direct line of sight to cost savings and product quality gains
Veera Padmanabhan
Head, Manufacturing Science & TechnologyAstraZenecaVeera is currently managing a team of 40+ scientists, establishing robotics, lab automation predictive analytics and advanced chemometrics in next generation biologics manufacturing at AstraZeneca. He has 25+ years of experience, with expertise crossing R&D, manufacturing operations and supply chain.
Afternoon SessionsLearn how AI-driven approaches integrate multiomics data, including genomics, proteomics, and transcriptomics, to identify potential drug targets and disease biomarkers for complex diseases.
Explore how AI models synthesize cross-omic data and real-time multiomic information to uncover novel biological mechanisms, identify potential biomarkers and enable precision medicine.
Kiran Nistala
Head, Functional GenomicsAlkermesExplore h ow AI models predict protein 3D structures from sequences, enabling insights into folding pathways and functional conformations
Examine emerging co-folding models that reveal protein–protein interactions and guide multimeric complex design.
Miles Congreve
Chief Scientific OfficerIsomorphic LabsExplore practical strategies for scaling AI implementation across clinical development pipelines, enabling faster trial execution, smarter protocol design, and improved patient recruitment while aligning with evolving regulatory expectations.
Maria Florez
Senior ConsultantTuftsLunchAfternoon SessionsExplore how AI enhances biomarker discovery by analyzing large datasets to uncover novel biomarkers for disease diagnosis and therapeutic efficacy.
Learn how integrating digital biomarkers with AI improves the interpretation of data from wearable devices and traditional lab-based biomarkers for better patient stratification and treatment personalization.
Satarupa Mukherjee
R&D Leader, AI/ML (Digital Pathology)Roche
Jack Geremia
CEOMatterworks
Virginia Savova
Senior Director, Head Cell-Targeted Precision MedicineAstraZenecaLearn how AI models enhance physics-based simulations to predict molecular interactions and optimize drug design.
Discover the synergy between machine learning and classical methods to accelerate screening and improve the accuracy of drug discovery.
Sreyoshi Sur
Former Scientist, Molecular Engineering & ModelingModernaShowcasing generative models that craft hyper‑personalized outreach messages and informed consent materials, driving up engagement rates and shaving weeks off recruitment timelines.
Discover how ML‑driven forecasts for recruitment rates and optimized site selection translate into faster first‑patient‑in and lower screen‑fail/dropout rates, saving you both time and budget.
Claire Zhao
Associate Director, AI/ML & Quantitative & Digital SciencesPFIZERHighlight how digital twins and hybrid ML models (e.g., Bayesian, predictive) enable virtual experimentation and proactive troubleshooting, reducing scale-up failures and supporting more reliable process performance at commercial scale.
Shruti Vij
Associate Director, Data Analytics & ModelingTakedaExplore how AI accelerates antibody discovery by enabling de novo design, epitope prediction, and in silico affinity maturation for highly specific, developable therapeutics.
Learn how deep learning and structure-based models optimize antibody stability, immunogenicity and target binding to advance precision biologics.
Adam Root
Vice President & Head, Protein SciencesGenerate Biomedicines
Claudette Fuller
Vice President, Non Clinical Safety & ToxicologyGenmabDiscover how ML and active learning techniques are revolutionizing the search for promising drug candidates in vast chemical libraries, accelerating hit identification.
Learn how AI models navigate ultra-large chemical spaces, prioritize bioactive compounds, and streamline the discovery of potential hits for further development.
Lingling Shen
Associate Director, Discovery SciencesNovartis
Justin Scheer
Vice President, In Silico DiscoveryJohnson & Johnson Innovative MedicineUncover how quantum technologies could reshape clinical trial design and optimization, from accelerating molecule-to-protocol timelines to improving patient stratification and adaptive trial modelling.
Michael Dandrea
Principal Data ScientistGenentech
Zoran Krunic
Principal Product ManagerAmgenSince joining Amgen R&D in 2018, Zoran Krunic has been at the forefront of applying Machine Learning to enhance patient outcomes and streamline clinical trial enrollment processes, utilizing comprehensive Electronic Health Records and clinical datasets. His pioneering work in the Quantum Machine Learning space, in collaboration with IBM's Quantum team, has been instrumental in integrating machine learning with quantum computing through IBM’s Qiskit platform.
Prior to his tenure at Amgen, Zoran developed Machine Learning algorithms at Optum to predict hardware and software failures within complex enterprise architectures. He has a strong background in data engineering and systems development, having contributed significantly to large-scale projects at renowned organizations such as Capital Group and ARCO Petroleum.
In his current full and part-time endeavors, Zoran is leading the efforts in embracing generative AI technologies, with a particular focus on OpenAI's GPT and Anthropic's Claude-2 models. His work is focused on prompt engineering and its application to code generation, advanced document analysis, and process management, with a commitment to ethical AI practices and data privacy.
A recognized voice in quantum computing circles, Zoran is a regular presenter at industry conferences and has served on numerous panels discussing the integration of quantum computing and generative AI within the Health Sciences sector.
With a Master of Science in Electrical Engineering & Computer Science, Zoran continues to explore and contribute to the evolving relationship between quantum computing and artificial intelligence, fostering groundbreaking advancements in healthcare technology.
Afternoon Break -
Thursday, 20 Nov, 2025Morning Sessions
This session provides the unique opportunity to listen to, and engage with, some of the most innovative AI Drug Discovery and Development start-ups globally. Focusing exclusively on early-stage funding, six startups picked by our esteemed selection committee will take to the stage in front of 100+ potential partners. Through a series of rapid-fire presentations, these pioneers will demonstrate their vision of the future of drug discovery, and how their product, technology, or service fits into it.
Morning BreakMorning SessionsDiscuss how Lab in the Loop is revolutionizing drug discovery by integrating AI with experimental workflows, enhancing speed and accuracy in data collection and analysis.
Shane Lewin
Vice President, AI & MLGSKExplore how AI-driven approaches enhance high-throughput screening by optimizing DNA-encoded libraries (DEL) for rapid identification of potential drug candidates.
Learn how AI algorithms accelerate the analysis of complex screening data, enabling more efficient lead discovery and targeting of molecular interactions.
Hans Bitter
Head, Computational SciencesTakeda
Jason Cross
Institute Director, Structural & Computational Drug DesignMD Anderson Cancer CenterGain actionable strategies for embedding generative AI and large language models into early-phase trial design and execution, from protocol drafting and site selection to patient engagement, accelerating timelines while ensuring data quality and compliance
Yi Hong
Senior ConsultantGileadHear cross-functional perspectives on successfully implementing AI across process development teams, from aligning with quality, IT, and manufacturing to overcoming cultural and technical barriers, with a focus on driving operational efficiency and long-term value
Ramila Pieres
Global Head, Data Management, ML/AI, MSATSanofiShruti Vij
Associate Director, Data Analytics & ModelingTakedaAfternoon SessionsExplore how AI-driven digital twins and functional models integrate patient-specific biology to identify and validate high-confidence drug targets by simulating system-level responses to genetic or pharmacological perturbations.
Learn how perturbation modelling with multiomic and functional genomics data predicts the effects of interventions on disease pathways, while LLMs synthesize data to uncover and prioritize novel therapeutic targets.
Zhiyong (Sean) Xie
Vice President & Head, AI & Data ScienceXellarbioExplore how AI accelerates the design of complex biologics, including ADCs and engineered cell therapies.
Learn how predictive models improve developability by forecasting linker stability, payload efficacy, and manufacturability.
Monica Wang
Head, Biologics & Novel Modality Discovery Capabilities & Products, Scientific InformaticsTakeda
Yorgos Psarellis
Senior Computational & Machine Learning ScientistSanofiGain actionable strategies for embedding AI and large language models into portfolio decision making, accelerating timelines while ensuring data quality and compliance
Gregory Goldmacher
Assistant Vice President, Clinical Research, Head, ImagingMerckLunchAn empowering session featuring inspirational speakers championing women’s leadership across tech, data, and pharma.
Ashwini Ghogare
Chief Executive Officer & Head, AI & Automation in Drug DiscoveryMilliporeSigma
Jackie Hunter
Chief Executive OfficerOI Pharma Partners
Petrina Kamya
President & Global Head, AI PlatformsInsilico MedicineAfternoon SessionsExplore how knowledge graphs integrate multi-source biological data, such as genetic, proteomic, and clinical information, into unified models that accelerate target discovery and disease understanding, with AI enhancing the extraction of actionable insights.
Learn how data normalization and the latest curation strategies ensure that biological datasets are clean, standardized, and AI-ready, enabling accurate analysis and improved model performance for drug development.
Daniyal Hussain
Executive Director, Technology Business DevelopmentGSK
Michael Steinbaugh
Director, Data, AI & Genome SciencesMerck
Shameer Khader
Executive Director, Precision Medicine & Computational BiologySanofiExplore how AI and large language models are revolutionizing reaction prediction, retrosynthesis planning, and synthetic accessibility scoring.
Learn how to evaluate and optimize AI-generated leads for real-world developability, including solubility, stability, and synthetic tractability.
Ethan Pickering
Head, Data Science & ML ResearchBayerExplore how generative AI is being used to analyze real-world data at scale, enabling earlier signal detection, automated safety reporting, and more dynamic risk-benefit monitoring, driving smarter, faster post-market decision-making across the product lifecycle.
Paul Petraro
Director, Real World Evidence AnalyticsBoehringer IngelheimDiscover practical strategies for scaling Process Analytical Technology (PAT) from R&D into regulated GMP environments , including method validation, data integrity, and cross-functional alignment to ensure continuity, compliance, and control at commercial scale.
Explore how AI-powered single-cell and spatial biology technologies reveal cellular heterogeneity, tissue organization, and microenvironmental interactions to uncover disease mechanisms and therapeutic targets.
Learn how AI models analyze high-dimensional cellular and spatial data to define pathogenic cell states, map dysregulated pathways, and prioritize targets for early-stage therapeutic discovery.
Qi Song
Principal Scientist, Predictive Biology & AIBristol Myers SquibbExplore how machine learning techniques, such as supervised learning and deep learning, predict critical ADME properties like solubility, permeability, and DDI risk.
Discover how computational methods, including molecular docking and quantum chemistry simulations, optimize high-affinity drug-target interactions for enhanced efficacy.
David Kombo
Principal ScientistSanofiEquip yourself with KPI dashboards and financial models to quantify time‑to‑value, optimize resource allocation, and build a compelling business case for AI investment.
Afternoon BreakThe Tech Test Lab features promising startups at Series A and earlier stages, offering a first look at bold, experimental technologies shaping the future of drug discovery
Closing Keynote
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