Artificial intelligence and machine learning models are used more and more often in the development of pharmaceuticals and as software components in medical devices. However, because there has been a lack of clear reporting standards, many clinically relevant models have been reported with insufficient details to properly assess their risks and benefits.

Historically, this has made the science underlying these products irreproducible, deployment and comparison of AI algorithmic solutions hard, and may lead to the users of these products facing unequal or unforeseen harms. Therefore a standard for reporting of biomedically-relevant AI/ML models is necessary. In this panel discussion we will brainstorm options for the transparent reporting of AI algorithms in biology and medicine.

Participants include Prof. Atul Butte and Dr. Beau Norgot, authors of the MI-CLAIM checklist recently published in Nature, Dr. Xiaoxuan Liu and Prof. Alastair Denniston, founders of the SPIRIT-AI and CONSORT-AI working groups, and Dr. Jen Harrow, Fotis Psomopoulos, and Dr. Tom Lenaerts, who are actively working on the standards for AI and ML in Europe.

Federated Learning is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA will present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, there will be a panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.

This presentation describes how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert. The implications of automation for the human-machine interface are explored and illustrated with examples from Bradshaw, GSK’s experimental automated design environment.

Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.

The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.

Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.

The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.

In the late Fall and Winter of 2018, the Pistoia Alliance in cooperation with Elsevier and charitable organizations Cures within Reach and Mission: Cure ran a datathon aiming to find drugs suitable for treatment of childhood chronic pancreatitis, a rare disease that causes extreme suffering.  The datathon resulted in identification of four candidate compounds in a short time frame of just under three months. In this webinar our speakers discuss the technologies that made this leap possible.

Prof. John Overington, the CIO of the Medicines Discovery Catapult describes the AssayNet project and its very far reaching implications.

Creating novel drugs is an extraordinarily hard and complex problem.

One of the many challenges in drug design is the sheer size of the search space for novel chemical compounds. Scientists need to find molecules that are active toward a biological target or pathway and at the same time have acceptable ADMET properties.

There is now considerable research going on using various AI and ML approaches to tackle these challenges.

Our distinguished speakers, Drs. Alex Tropsha and Ola Engkvist, will discuss their recent work in Drug Design involving Deep Reinforcement Learning and Neural Networks, and will answer questions from the audience on the current state of the research in the field.

Data Quality is at the heart of AI/ML and is one of the key challenges we face in Life Sciences to make AI a success.

In this panel we discuss how to address these data quality issues and what steps we need to take to improve things in the future. We will also look at practical ways to improve things now.