Pharma Foucs Europe
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Webinar Abstract:

Antibody drug discovery faces significant challenges in optimizing workflows, reducing timelines, and ensuring the successful development of therapeutic antibodies. With increasing complexity in drug targets and growing pressure to innovate, the need for more efficient, scalable solutions is greater than ever. Artificial intelligence (AI) is poised to address these challenges by streamlining the process and enhancing decision-making at each stage. This webinar will explore how AI can be integrated across the Design–Make–Test–Decide (DMTD) workflow, featuring insights from three key perspectives:

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  • The Biotech/Pharma Perspective will focus on the antibody discovery phase, discussing the design of optimized workflows to overcome early-stage bottlenecks and improve development speed.
  • The CRO/Service Provider Perspective will explore how AI-powered tools and datasets are enabling more efficient antibody production, improving the chances of success in downstream development.
  • The Scientific Software Provider Perspective will highlight how AI-driven software solutions, like developability dashboards, can proactively address issues during the development phase, ensuring smoother transitions and fewer costly setbacks.

The webinar will conclude with a dynamic roundtable Q&A session, offering attendees the opportunity to engage with the experts and discuss the challenges and opportunities in AI-driven antibody discovery.

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Why Attend:

  • Gain Insights from Diverse Perspectives: Learn from experts in biotech, CROs, and scientific software on optimizing the antibody discovery process.
  • Discover Cutting-Edge AI Tools: Understand how AI and datasets are revolutionizing the design, make, and development of antibodies
  • Master the DMTD Workflow: Get a clear understanding of how each stage of the antibody discovery process works and how to tackle challenges effectively.
  • Optimize Your Discovery Process: Learn how to use software tools to catch potential development issues early, saving time and resources.
  • Engage in Interactive Discussions: Participate in a roundtable Q&A session with industry experts and get your specific questions answered.

Who Should Attend:

  • Pharma and Biotech Professionals: Scientists, R&D leads, and managers involved in antibody discovery, development, and optimization.
  • CROs and Service Providers: Professionals working in collaboration with pharmaceutical companies on antibody production and development.
  • Scientific Software Providers: Individuals interested in software solutions that enhance the efficiency of antibody discovery and development workflows.
  • AI Enthusiasts and Innovators: Those exploring the application of AI in biotech and drug discovery.

Speakers:

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Zev Wisotsky
PhD, Senior Principal Marketing Manager, Biologics Solutions, Revvity Signals

Zev Wisotsky is a Senior Principal Marketing Manager for Biologics Solutions at Revvity Signals. His scientific training and research background include neuroscience, biochemistry, molecular biology and drug discovery. He has spent almost 10 years in software in go-to-market teams across industries with a heavy focus on biopharma/biotech R&D.

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Sibo Tao
PhD, Scientific Director, Global Biologics Service, Sanyou Biopharmaceuticals

Sibo Tao is a Scientific Director for Global Biologics Service at Sanyou Biopharmaceuticals. Her scientific training and previous experience include molecular biology, protein biochemistry and antibody drug discovery. At Sanyou Biopharmaceuticals, she leads multiple antibody drug R&D projects from early discovery to functional screening and optimization.

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Arjan Hada
Sr. Scientist, Machine Learning Bioinformatics , iBio

Arjan Hada is a Senior Scientist at iBio, specializing in AI-driven antibody discovery. He combines computational protein design with experimental validation to accelerate therapeutic development. With experience across the biopharma industry, he earned his Ph.D. from Southern Illinois University Carbondale, studying multisubunit protein complexes using biochemistry, genomics, and proteomics.