Developing a mechanistic model of IVT to include nucleation and growth of magnesium pyrophosphate crystals and subsequent agglomeration of crystals and DNA.
Nathan Stover
PhD Student, Chemical Engineering
Massachusetts Institute of Technology
Forges powerful ecosystems by aligning pharma, tech, and academia, enabling shared expertise and resources to accelerate breakthroughs and navigate complex R&D challenges.
Explore 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.
Attila Csikász-Nagy
CEO
Cytocast
Attila is a visionary leader with a strong background in computational and systems biology. As a professor and researcher, he has made significant contributions to the field, with an impressive publication record and expertise in bioinformatics. With experience at renowned institutions like Microsoft Research and King's College London, Attila brings a unique blend of scientific knowledge and business management skills to his role as CEO. In addition to his professional pursuits, Attila enjoys playing basketball competitively with his old high school friends.
David Kombo
Principal Scientist
Sanofi
Jacob Berlin
CEO
Terray Therapeutics
Cytocast
Website: https://www.cytocast.com/
Cytocast, a TechBio startup based in Budapest, Hungary, accelerates drug discovery by combining bioinformatics, AI, and large‑scale digital‑twin simulations to predict side effects at the start of R&D. Pharma, biotech and CRO teams use the Cytocast technology to de‑risk portfolios earlier and focus resources on safer candidates.
The CYTOCAST DIGITAL TWIN Platform™ models cellular systems to deliver breadth‑first side‑effect profiling for early‑stage small‑molecule programs, including combinations. Proteome‑based simulations reveal how candidates perturb cellular processes and provide actionable evidence to refine chemistries and improve safety. The platform has been validated through pilots with small and mid‑size partners. Currently, the following products are available for the clients’ tailored needs:
- Cytocast Screener™: Rank thousands of compounds by likely side effects to guide early discovery.
- Cytocast Optimizer™: After in vitro/in vivo, refine leads with comparative safety insights for preclinical decisions.
- Cytocast Nominator™: In‑depth analyses to support final candidate selection for trials.
Cytocast has raised €2.5M pre‑seed to date. We are opening a new €5M seed to expand product capabilities and market reach with the goal of adoption at least 10 top‑100 pharma companies by 2027. Cytocast is a proud member of the Alliance for Artificial Intelligence in Healthcare (AAIH) and NVIDIA Inception.
Lana Garmire
Associate Professor
University of Michigan
Safeguards the innovation pipeline by proactively securing sensitive research data, enhancing risk resilience and ensuring stakeholder confidence in the integrity of AI-driven discoveries.
Explore 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 Research
Bayer
Explore 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 Development
GSK
Mark Kiel
Chief Science Officer
Genomenon
Mark Kiel, MD, PhD, and Molecular Genetic Pathology Fellow at University of Michigan, is the founder and CSO of Genomenon, where he oversees the company’s scientific direction and product development. Mark's passion is to power the practice of precision medicine by organizing the world’s genomic knowledge. To that end, he created Genomenon and the Mastermind suite of genomic tools.
Aaron Daugherty
Associate Director, Computational Biology
BridgeBio
Genomenon
Website: https://www.genomenon.com/
Genomenon unlocks valuable real-world evidence buried in clinical literature to inform genetic disease and cancer research. Our data and insights empower precision therapeutic companies to optimize clinical trial design, support label expansion, enhance diagnostic patient yield, and streamline regulatory submissions.
Genomenon uses its AI knowledge graph to mine over 10 million full-text scientific articles to characterize patient data reviewed by its team of scientific experts. This comprehensive approach transforms previously inaccessible data into actionable insights, enabling refined disease-prevalence estimates, genotype- phenotype correlation discovery, and clarifying patient demographics and treatment outcomes.
Genomenon's RWE approach unlocks the vast repository of published research, capturing billions of dollars' worth of insights into rare disease and cancer patient presentations, clinical journeys, treatments, and outcomes.
Hear 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, MSAT
Sanofi
Shruti Vij
Associate Director, Data Analytics & Modeling
Takeda
Harnesses collaborative innovation networks to integrate external expertise and accelerate breakthrough AI development.
Dive deep into how large language models are automating complex planning tasks, from trial feasibility assessments and synthetic protocol generation to cross-functional alignment and regulatory-ready documentation, with real-world examples of scalable implementation and measurable impact.