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.
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.
Explore 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.
Explore 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.
Equip teams with AI tools that capture process knowledge and simulate scale-up scenarios, reducing tech transfer timelines and improving first-batch success rates - critical for aligning R&D, MSAT, and manufacturing expectations early.
Gain 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
Reinforces trust and compliance by embedding robust data governance frameworks and ethical AI practices, to responsibly harness data and mitigate future regulatory risks.
Explore 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.
Discuss how Lab in the Loop is revolutionizing drug discovery by integrating AI with experimental workflows, enhancing speed and accuracy in data collection and analysis.
Generoso Ianniciello is a strategic leader in life sciences with expertise in multi-omics, diagnostics, biopharma, and platform services. As Chief Business Officer at Anima Biotech, he leads the global strategy for the Biology GPU - a new category in drug discovery infrastructure that connects how AI thinks with how biology works, tackling one of the biggest challenges in the field.
Previously, as CBO at Dante Genomics, he scaled the start-up into a global enterprise with over 200 employees and $100 million in annual revenue. He launched the Dante MyGenome Platform for longevity, personalized medicine, and rare disease diagnostics, and built key partnerships with hospitals, biopharma, and research institutions worldwide.
Anima Biotech has built the biology runtime for AI - the Biology GPU.
The Biology GPU is the infrastructure layer that connects how AI thinks with how biology works. It enables AI models to visually compute inside cells in a closed loop of experimental reasoning, revealing the pathways that drive disease.
Powered by a Visual Biology Model trained on 2B+ proprietary cellular pathway images and technology that directly visualizes biological processes and pathways at AI scale, the Biology GPU runs visual experiments inside cells - identifying which pathways are active, how they interact, and where compounds act.
From pathways to targets, mechanisms to compounds, the Biology GPU transforms discovery into a runtime where AI engages directly with biology across the entire lifecycle.
Validated in 20+ discovery programs and collaborations with AbbVie, Takeda, and Lilly, Anima Biotech is defining the runtime infrastructure for AI in biology.
