Generative AI in Drug Discovery

Oct 26th, 2023
Virtual Event, 1 PM - 4 PM, EST
Connect with the leading minds and innovators in Pharma, Research, Biotech and AI to discuss Generative AI’s limitless potential, promising signs of applications, and the possibilities this model can bring to make drug research less laborious.

Learn More About DataFAIR 2023

Agenda for DataFAIR 2023

Keynote Sessions
Data Quality: The Cornerstone of Effective Generative Al in Drug Discovery

Co-Founder & CEO, Elucidata

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Gen AI has the potential to transform the drug discovery field. However, these  models need to be trained with quality datasets before they can be productionized. Using an inadequately trained model in this context can result in inaccurate predictions, unviable outcomes, and significant project expenses. In this session, Dr. Jha discusses the importance of data quality in training Gen AI models and its role in enhancing the robustness and reliability of target prediction in the pharmaceutical industry.

Gene Set Analysis with Generative AI

Professor, Icahn School of Medicine at Mount Sinai

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In this keynote session, Dr Ma'ayan delves into the transformative impact of generative AI and LLMs in the analysis of gene sets. He discusses how these innovations are enhancing gene annotation prediction, ultimately leading to the discovery of novel therapeutic targets across a broad spectrum of biomedical research applications.

FAIR Data Governance and Generative AI

Senior Adviser to the Milken Institute's FasterCures

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Our keynote speaker, John Wilbanks is a distinguished authority in the field of data governance, backed by a wealth of experience and a forward-thinking vision regarding the future of data and AI. In his session, he sheds light on the hurdles and prospects of effectively implementing data governance strategies that unlock the potential of generative AI.

Product Talk
Tech Stack for GenAI in Early-stage Life Sciences R&D

Co-Founder & CTO, Elucidata

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GenAI is as promising as it is challenging. To stay on top, scientists will have to combine data, infrastructure, models, and subject-matter expertise into a formidable base. While Gen AI is incredibly exciting, what does it really take to get these models into production? How can we keep trusting the results? How does one select the right problem? In this session, Swetabh gives us a breakdown of the tools needed to set your GenAI initiatives up for success.

Panel Discussion
GPT & Drug Discovery: Rise of Generative Models

Chetanya Pandya

Global Head of Data Engineering, Oncology, Sanofi

Panelist

Ming Tommy Tang

Director of Computational Biology, Immunitas Therapeutics

Panelist

Helena Deus

Principal, Technology Consulting, EPAM Systems

Panelist

Prashant Natarajan

Vice President, Strategy and Products, H2O.ai

Panelist

Jainik Dedhia

Senior Product Manager, Elucidata

Panelist

Vladimir Makarov

Innovation Leader in Computational Biology

Moderator

The panel elucidates on the rise of generative models in drug discovery.Topics covered:

  • The applications of these models in different drug discovery domains - genomics, structural biology, and protein design.
  • The central role 'data quality' plays in the successful use of these models.
  • How the panelists are incorporating (or are planning to incorporate) generative models into their R&D data strategy.
  • And the challenges they've faced while applying these models in production.
Customer Fireside Chat
Challenges in Data Management in Early-stage Life Sciences R&D

Christopher Plescia

Director, Clinical Biomarker Technical Lead, Hookipa Pharma

Xitong Li

Chief Technology Officer,
NextGen Jane, Inc.

Neychelle Fernandes

Director of Solutions and Technical Sales, Elucidata

Moderator

The fireside chat focuses on data management strategy in early stage R&D. Effective data management plays a pivotal role in the pharmaceutical industry. In this conversation, our panelists will cover:

  • Effectively managing multi-modal, multi-source data, including CROs, patient data, and in-house data.
  • The costs incurred without the right data infrastructure.
  • And addressing data downtime, curation costs, and cloud solutions.

Why Attend

Hear from the best minds in the Biopharma/ Biotech space to explore the revolutionary potential of Generative AI in drug discovery. From information extraction to biological insights, Generative AI shows many promising results but will this translate into production-ready models and impactful use cases? Let’s pressure test the hype vs. reality!

About

DataFAIR is Elucidata’s annual event that talks about data-centric AI, FAIR data for insight, use of LLMs, cutting-edge solutions etc., to accelerate research and drive the future of drug discovery. For 4 years now, DataFAIR has united a community of leading life sciences and pharmaceutical experts in the fields of biomedical research, and drug discovery & development from around the world. This year, the event will focus on the potential of Generative AI and elucidate on the numerous possibilities that the model holds for drug R&D. Come be a part!

All Speakers

Avi Ma'ayan
Icahn School of Medicine
Professor, Icahn School of Medicine at Mount Sinai
Chetanya Pandya
Sanofi
Global Head of Data Engineering, Oncology
Helena Deus
EPAM Systems
Principal, Technology Consulting
Prashant Natarajan
H2O.ai
Vice President, Strategy and Products
Ming Tommy Tang
Immunitas Therapeutics
Director of Computational Biology
Christopher Plescia
Hookipa Pharma
Director, Clinical Biomarker Technical Lead
Xitong Li
NextGen Jane, Inc.
Chief Technology Officer
John Wilbanks
Milken Institute's FasterCures
Senior Adviser
Abhishek Jha
Elucidata
Co-Founder & CEO
Swetabh Pathak
Elucidata
Co-Founder & CTO
Neychelle Fernandes
Elucidata
Director of Solutions and Technical Sales
Jainik Dedhia
Elucidata
Senior Product Manager

Highlights from Previous Years

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