Artificial Intelligence in drug discovery Info Session

Artificial intelligence is widely predicted to make drug discovery and patient diagnosis quicker, cheaper and more effective in the future, and signs of this can already be seen. AI has the potential to expedite the clinical drug discovery and development process by applying sophisticated algorithms to the analysis and mining of different data sources to predict molecule behavior and suitability as drug targets or therapeutic entities. McKinsey estimates that big data and machine learning in pharma and medicine could generate up to $100bn annually, based on better decision-making, optimised innovation, improved efficiency of research, clinical trials, and new tool creation for all stakeholders. This info-session organised by PIEP will cover different approaches and AI platforms, and how they will impact the pharmaceutical industry and specifically, the drug discovery and development processes. Join us to learn more about how these companies operate, what their objectives are and how you can get involved (there will be an hour of networking and several are hiring!). Food and drinks (alcoholic and non-alcoholic) will be provided during the networking section.

Date: Thursday, September 27th

Time: 6:00 - 8:30 PM

Agenda: 6:00 - 7:00 PM - Scientific Talks

7:00 - 7:30 PM - Panel Discussion

7:30 - 8:30 PM - Networking & Dinner

Location: Stanley Hall Room 106

Speakers: Izhar Wallach, CTO and Co-Founder, Atomwise.

Jeff Warrington, Medicinal & Computational chemist, Atomwise.

Raj Bhatnagar, Computational Biologist, Verge Genomics.

Katja Hebestreit, Computational Biologist, Verge Genomics

Brian Bocchino, Director of Recruiting at Freenome

Participating Companies

Atomwise (www.atomwise.com), Artificial Intelligence for Drug Discovery

Atomwise design new molecules for the hardest targets. By using convolutional neural networks – the same AI technology that recognizes faces in a crowd, enables self-driving cars, and allows you to talk to your phone. Their technology uses a statistical approach that extracts insights from millions of experimental affinity measurements and thousands of protein structures to predict the binding of small molecules to proteins. This fundamental tool makes it possible for chemists to pursue hit discovery, lead optimization and toxicity predictions with unparalleled precision and accuracy.

Freenome (www.freenome.com), Pioneering AI Genomics

Freenome is using artificial intelligence (AI) to recognize disease-associated patterns among billions of circulating, cell-free (cf) biomarkers, Freenome is developing simple, accurate, and noninvasive blood tests for early-cancer screening and treatment selection.

Verge Genomics (www.vergegenomics.com), Transforming Drug Discovery using Artificial Intelligence

Verge was founded by a unique combination of the field's top machine learning experts and seasoned neuroscience drug developers. Together, they share the vision that exponential advances in computational genomics combined with new insights into neuroscience has created a breakthrough opportunity to discover drugs that dramatically improve the lives of patients suffering from neurodegenerative disease.