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Gerd Blanke
Technical director, StructurePendium Technologies GmbH
Are reaction data FAIR … and what can we do with that?
Gerd talks about how chemical reaction data needs to comply with FAIR principles in order to be reliable, reproductible, and be used for accurate AI/ML predictions. He addresses the specific challenges related to in-house chemical reaction data capturing and discuss what steps should be taken to make these databases more consistent for precise predictions.
Joe Michel
Director of Informatics, Cytokinetics
Generative AI Begins to Dominate the AI Conversation in Early Drug Discovery
In his presentation, Joe focuses on how deep neural networks and generative AI are transforming molecular design, improving predictions, and enhancing the drug discovery process.
John McNeil
Life Science R&D Informatics Strategist
Project team data visualization and decision support solutions - Core requirements and must-have features
John talks about the core requirements and must-have features of collaborative data visualization and decision support solutions in the DMTA cycle.
Zofia Jordan
Compound Compliance Consultant, formerly of GSK
Advocating Change: One Synthetic Cannabinoid at a Time
Zofia talks about how compliance aspects associated with sample management are becoming more critical due to the collaborative and globally distributed nature of research. She showcases it through her experience working with the industry and authorities to modify the Third Generation Synthetic Cannabinoids amendment.
David Klatte
Senior Director, Scientific Solution Engineering, Pfizer
ChemistryView: how Pfizer simplifies its legacy desktop chemistry systems
David introduces ChemistryView, Pfizer’s custom developed Java component designed to simplify their legacy chemistry software systems. ChemistryView stemmed from a strong desire for the company’s often 20-years old applications to be user customizable and to have consistent tools for managing advanced stereochemistry.
Dr. Arun Subramaniyan
Vice President Cloud & AI, Strategy & Execution, Intel
World’s Largest Protein-Ligand Complex and Binding Affinity Dataset for Data Driven Methods in Drug Design
Arun uncovers a collaborative, multi-phase research project involving Intel, AWS, Insilico Medicine and IIITH with the ultimate aim of creating the world’s largest protein-ligand complex and binding affinity dataset that significantly accelerates drug research and discovery timelines.