AI in Drug Discovery Conference 2020

SMi Group presents their inaugural: AI in Drug Discovery conference 2020 is organized by SMi Group Ltd and will be held from Mar 16 - 17, 2020 at Copthorne Tara Hotel London Kensington, London, England, United Kingdom.

Who Should Attend:
VP, Head, Manager, Director, Scientist in
• Artificial Intelligence
• Machine Learning/Deep Learning
• Drug Discovery
• R&D
• Medicinal Chemistry
• Cheminformatics
• Computational Chemistry
• Molecular AI
• AI design

Conference Overview:
AI-empowered machine learning technologies hold the potential of reducing drug discovery associated costs by US$ 70 billion in the upcoming 10 years. With an estimated +39% CAGR, AI in drug discovery is leading the way into a shorter, cheaper and more successful R&D era where compound generation is automated, drug synthesis is predictable and undruggable diseases are finally being targeted.

The presence of AI in drug discovery is tangible with the majority of drug discovery scientist already working with AI-enabled platforms using machine learning and deep learning, neural networks and natural language processing. However, there is a long journey ahead of optimizing AI-human connections and understanding the full potential of AI-enabled tools and platforms.

Those who work in the field know that there is no AI revolution without tackling the field’s number one challenge: DATA. It is crucial now more than ever to come together and discuss strategies to achieve data revolution for further advancing R&D.

Join us at SMi’s inaugural AI in Drug Discovery 2020 Conference and explore the latest AI-enabled approaches for lead compound screening, multi parameter optimization, disease modelling, drug synthesis and design.

Benefits of Attending:
• Listen to case studies form industry leader pharmaceutical and biotechnology that have already incorporated AI into their work
• Explore how Deep Learning Methods can be leveraged for compound screening, de novo design, multiparameter optimization/ ADME toxicity property predictions, chemical synthesis route predictions
• Discover strategies for overcoming data-related challenges such as lack of consistent and quality data at the heart of AI and strategies for improving data access
• Define unique discovery approaches such as fragment-based drug discovery and network-driven drug discovery 

Additional details will be posted as soon as information is available.