Transforming Drug Discovery through AI Agenda 2021


Day One l 6th JULY 2021

9am ET Dotmatics Platform & Adventures in AI
Dotmatics is an industry leading cloud-based scientific R&D data management platform. An ambition of Dotmatics is to make all the research data available via querying and reporting. In building the querying and reporting features we have also built the perfect springboard for adventures into AI.
This talk will cover the current crop of practical AI/ML prototypes available in the Dotmatics platform and some early plans to automate AI/ML techniques over all the data our customers store in our platform. We will cover the use of WebAssembly for edge computing/client-side deployment of models and some interesting observations of the benefits of the new AWS x2gd instance types for accessing larger (2 billion+) sets of purchasable compounds.

Dan Ormsby, Senior Consultant, Dotmatics
10.00am EST How FAIR laboratory data can unleash the promise of AI 
  • Analytical data lakes - why it is not ready
  • Train AI
  • What it needs to become
  • Standardised data
  • Cooperative standardisation – ASTM approved.
  • The power of
Session Reserved for Haydn Boehm, Head of Commercial Marketing -Connected Lab, Merck Group
11.00am EST Transforming Protein Engineering Through AI Enabled Smart Connected Labs
To accelerate scientific discovery, our industry must re-imagine laboratories as smart and automated data-rich centers to generate clean, machine actionable, and reproducible data rapidly as compared to today’s artisan (and mostly manual) research approaches
Cloud access to smart connected laboratories is no longer a reality waiting to happen. This newer breed of laboratories is ushering in new knowledge driven by data, computation, artificial intelligence, automation, and high-throughput robotics with the goal of fundamentally advancing the life sciences
Learn how the University of Wisconsin remotely leveraged and integrated the Strateos automated robotic cloud lab, and created a fully autonomous robotic discovery platform driven by artificial intelligence that designs and screens protein sequences in a reproducible and iterative manner, making the long process of protein engineering much faster and more reliable.

Session Reserved for Strateos

End of Day One 

Day Two l 7th JULY 2021
9am ET Session Reserved for IDBS

10am ET Session Reserved for BIOVIA

11am ET Understanding the Practical Challenges of Implementing AI
  • What is surrounding AI in drug discovery?
  • What can we do with data to enhance efficiency?
  • How are we validating processes and running tests and reviewing data?
  • What infrastructure challenges have been faced so far and how are we trying to overcome these hurdles
  • What are the next steps for Enginzyme?
Nadim Morhell, Director of Automation and Data Management, EnginZyme

End of Day Two

Day Three l 8th JULY 2021

9am ET Practical Applications of Deep Learning to Enabled Decision Making for Small Molecule Drug Discovery Data
Small molecule drug discovery is the iterative process of identifying starting compounds and improving them through multi-parameter optimization (MPO). This session will address:
  • How can we improve upon the current approach?
  • How can we balance this approach mostly driven by human experts with some assistance from computational approaches?
  • How to gain more value from compound data, filling gaps and identifying complex relationships

Session Reserved for Suneel Kumar, Director of Computational Chemistry, AI, Sai Life Sciences

10am ET FIRESIDE CHAT: Applications of AI in the Discovery of Targets
At Alnylam Pharma we have been working in partnership with the UK Biobank to assess genetic and phenotypic data, biomarker data, MRI scans and medical imaging to understand which genes to go after for a particular disease. We have specific use cases and lessons learned and this fireside chat will discuss:
Repurposing technology to train neural network models
How to ascertain which genes to go after when scanning data?
How can we link this to genetic data?

Paul Nioi, Vice President, Discovery and Translation Research, Alnylam Pharma  

11am ET JOINT CASE STUDY: Patent Scope Visualization: A Patent Mining and Visualization Platform for Antibody Claims
This session will address:
  • Importance of patents for scientific and legal teams
  • Overview on extracting key epitope data from antibody patents
  • Novel visualization showing the claimed epitopes in context

Peter Henstock, AI and ML Lead, Pfizer + Harvard 

End of Day Three


Leveraging data to reduce the cost, time to market
 and failure rate of the traditional drug discovery process

It is a commonly quoted statistic that the average cost to bring a new drug to market is 2.6bn, and that only 10% of drug candidates make it to market after phase 1 trials.

Given its potential to spot patterns that would take researches significantly longer to do, Artificial Intelligence has the potential to both accelerate and reduce the cost of discovering new drugs.

With that in mind, PharmaIQ Live: Transforming Drug Discovery through AI will be focused on:

  • Target identification – combing existing data to identify potential drug targets
  • Compounds – identifying existing compounds that might prove useful, ruling out those with undesirable characteristics and predicting the properties of potential compounds
  • Data integration – effectively integrating data from disparate sources such as patient data, research papers to identify patterns and opportunities
  • Enhancing efficiency – by removing the burden of high volume, low value tasks in the lab
  • Training – upskilling existing drug development scientists on how best to use AI to best effect

Who should attend?

Pharma IQ Live is designed for Heads / General Managers / Directors of 


Artificial Intelligence




Drug Discovery


Discovery Informatics


Research and Development


Science & Technology

Efficiency & Productivity

AI & Machine Learning

Data Integrity & Security

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Gemma Mills
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