AI for Drug Discovery

The use of artificial intelligence in biopharma has redefined how scientists develop new drugs, tackle disease, and more. The AI for Drug Discovery track will discuss problems that biopharma organizations are experiencing in harnessing the power of AI and machine learning technologies to maximize and accelerate drug discovery efforts from early stage to adoption to practical application. Speakers will explore the role of AI in developing new drugs, tackling diseases previously deemed too difficult to take on, the R&D process, chemical synthesis optimization, drug repositioning, making sense of clinical data, predicting clinical trial outcomes, finding correct patients for clinical trials, analyzing real-world evidence, making sense of complex medical data, and avoiding "brittle" results based on too little/biased data. How are you moving beyond correlative analysis towards causal analysis so that these approaches can become generally effective? How do you integrate the challenges that exist in the real-world practice of medicine… and with real patients?

Preliminary Agenda

AUTOMATED AI-GUIDED DRUG DISCOVERY LABS

Towards AI-Guided Cell Profiling of Drugs with Automated High-Content Imaging

Ola Spjuth, PhD, Associate Professor, Department of Pharmaceutical Biosciences, Uppsala University

USING AI TO PROPEL THE DRUG DISCOVERY AND DEVELOPMENT PIPELINE

Progress in Diagnosing Rare Disease Patients Leveraging NLP

Tom Defay, Senior Director, R&D Strategy and Alliances, SPMD, Strategy, Program Management and Data Sciences, Alexion

Accelerated Drug Development Using AI

Bino John, PhD, Associate Director, Data Science - Clinical Pharmacology & Safety Sciences, AstraZeneca R&D

Machine-learned Molecular Models for Protein Structure, Networks, and Design

Mohammed AlQuraishi, PhD, Systems Biology Fellow, Harvard Medical School


Platinum Sponsors

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linguamatics

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