AI and Computational Drug Discovery (Training Workshop)




This novel online training workshop will explore how artificial intelligence (AI) is driving change within drug discovery. We will explore both predictive and generative machine learning approaches. This training course is set to an introductory level for a scientific audience and focuses on the application rather than the coding. Breakout activities will allow you to understand and apply your knowledge using real-life examples from within industry. 

Learning Outcomes:

Following the course, you will be able to:

  1. Explain the current uses of AI and machine learning within the context of drug discovery

  2. Identify the limitations of AI and machine learning and be aware of the future possibilities for AI in drug discovery

  3. Have a basic understanding of how generative models can be used for compound design

  4. Describe the key elements for reliable machine learning predictive models for a given range of physicochemical properties or receptor interactions


You can download a copy of the programme here
 

10:15 - 10:25

Welcome and plan for the day
Dr Martin Redhead
 

10:25 - 10:50

Demystifying AI: Intuition for how it works, when it works, and what to look out for
Dr Angelo Pugliese
 

10:50 - 11:15

Biological graph databases - information transfer for AI
Dr Alan Bisland
 

11:15 - 11:40

Generative models for small molecule drug discovery
Aleksandra Kalisz and Dr Francesca Vianello
 

11:40 - 12:00

Break
 

12:00 - 12:30

Breakout Session
 

12:30 - 12:55

AI to solve difficult kinetic problems in receptor pharmacology
Dr Martin Redhead
 

12:55 - 13:40

Lunch
 

13:40 - 14:10

Breakout Session
 

14:10 - 14:35

Model-based target pharmacology assessment (mTPA) - how AI can inform strategies for drug discovery
Dr Emile Chen
 

14:35 - 15:05

Breakout Session
 

15:05 - 15:20 

Break
 

15:20 - 15:45

Quantitative systems pharmacology target modulation and clinical outcomes
Dr Valeriu Damian
 

15:45 - 16:15 

Breakout Session
 

16:15 - 16:20

Wrap-up and revisit of learning outcomes
Dr Francesca Vianello
 

You can view and download a copy of the speaker biographies here

Dr Martin Redhead, Exscientia
Dr Martin Redhead is Head of Quantitative Pharmacology at Exscientia, and a highly engaged BPS member. Martin has also worked at University College Birmingham (UCB) and Sygnature Discovery in their pre-clinical pharmacology / DMPK departments. Martin's research interests are in the use of Artificial Intelligence and computer software to model and solve difficult kinetics problems in receptor pharmacology.

Dr Francesca Vianello, Exscientia
Dr Francesca Vianello is a Structural Bioinformatics Research Scientist at Exscientia. Her research interests are Computational characterisation of protein interaction sites. She has applied graph-theoretical methods to the discovery and structural investigations of protein-protein interactions. She has experience at Imperial College London, and the Massachusetts Institute of Technology. 

Dr Angelo Pugliese, BioAscent
Dr Angelo Pugliese is Associate Director of In Silico Discovery and Data Analysis at BioAscent Discovery, a leading provider of integrated drug discovery services. Prior to joining BioAscent, Angelo led the computational chemistry and artificial intelligence team at the drug discovery unit at the CRUK Beatson Institute in Glasgow. He is a highly experienced computational chemist, project leader and strategic contributor to discovery teams and has over 15 years experience working across different organisations in the US and the UK. 
He has a PhD in Computational Chemistry from the University of Nottingham.  


Dr Alan Bisland, Exscientia
Dr Alan Bisland is a Discovery Data Scientist at Exscentia and an Honorary Lecturer at the University of Glasgow. He has over 10 years experience of working with machine learning and computer modelling in the field of cancer therapeutics drug discovery. Alan's main interest is to make a better critical path in translational oncology than that which currently exists. 

Aleksandra (Ola) Kalizs, Exscientia
Aleksandra Kalisz (Ola) is a Senior AI Research Engineer at Exscientia and has a background in Artificial Intelligence and Computer Science, having studied at the University of Edinburgh and Caltech. Ola is particularly interested in exploring research opportunities across the full breadth of machine learning both in industry as well as across academia. 

Dr Emile Chen, GlaxoSmithKline
Dr Emile Chen is a Director of System Modelling and Translational Biology at GlaxoSmithKline. He has worked in the pharmaceutical industry for over 20 years working in drug metabolism and pharmacokinetics on a wide variety of targets and therapeutics areas both clinically and pre-clinically. Emile has worked with GlaxoSmthKline, Glaxo Wellcome and Roche. 

Valeriu Damian, GlaxoSmithKline
Valeriu Damian is Director if Pharmacokinetics and Translational Biology at GlaxoSmithKline. He has over 20 years of experience working in the drug discovery industry. Valeriu has a background in Engineering and Mathematics but soon became interested in machine learning and computational modelling of biological systems and has used this interest for a career in the pharmaceutical industry. 

Dr Frauke Breitgoff, Exscientia
Dr Frauke Breitgoff is a Biophysical Data Scientists in Drug Discovery at Exscientia. She has a PhD in physical chemistry with experience in assay development, data processing and software development. 

Tickets


Member Registration Member Ticket £145.00
Non-Member Registration Non-Member Ticket £195.00
From
22 June 2023
To
22 June 2023
Time
10:15 GMT to 16:20 GMT



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