Augmented Intelligence in Drug Discovery Xchange
Europe 2021, December 3rd

Welcome to hubXchange’s European Augmented Intelligence (AI) in Drug Discovery Xchange 2021, bringing together executives from pharma and biotech to address and find solutions to the key issues faced in AI-led drug discovery.
Discussion topics will cover Data Quality, Target Identification, Lead Generation, Lead Optimization and Drug Response Prediction.
Take advantage of this unique highly interactive meeting format designed for maximum engagement, collaboration and networking with your peers.

Please note this is now a fully VIRTUAL meeting.

Data Quality

Time

Titles and Bullets

Facilitator

8:00 – 8:30am

Registration

8:30 – 9:00am

Opening Address & Keynote Presentation

Closing the data-insight gap

The volume of biological data is growing exponentially
But analytic capabilities are not keeping up – broadening the data-insight gap
Computational disease models can help. They’re designed to process high throughput and support R&D in every phase of the drug lifecycle

Shai Shen-Orr

Co-founder and Chief Scientist
CytoReason

Professor Shai Shen-Orr is the Co-founder and Chief Scientist of CytoReason, a technology company developing computational disease models. He serves as Associate Professor in the Faculty of Medicine at the Technion—where he directs the laboratory of Systems Immunology and Precision Medicine. In his research, Prof. Shen-Orr develops new analytical methodologies for grappling with the intricate complexities of the immune system, especially as they occur in advanced age, and to define biomarkers to evaluate immune health. His work has been featured and cited in numerous top-tier journals and systems biology textbooks, and has laid the foundation for CytoReason. To date, six of the world’s top ten pharma companies use CytoReason’s technology to speed up drug discovery and dramatically cut the cost of developing new medicines.

9:05 – 10:05am

The road to achieving seamless operational ML – how to solve the last mile of ML delivery

What lessons can we implement from other industries?
How to move from PoC (Data Science work) to enterprise ready ML models in production
Which skills do we need (ML engineer vs. data engineer, vs data science), how to manage the handover (self-service vs. managed service)
Discuss the training aspects to enable organizational readiness for ML

Anna Bauer-Mehren

Head Data Science, pREDi
Roche

Anna is a Bioinformatician by training and leads the Data Science Department in Roche Pharma Research and Early Development in Germany. Her team supports pre-clinical and clinical research teams through data management and data analysis, applying in particular Artificial Intelligence methods. We are analyzing high-dimensional data such as imaging data, genomic information, and data from electronic health records to better understand diseases and develop personalized therapies.

Anna holds a PhD in Bioinformatics and Biomedical Informatics from the University of Pompeu Fabra in Barcelona and Master in Bioinformatics from the Ludwig-Maximilians-Universität and the Technische Universität München. She completed her postdoc education at the Faculty of Biomedical Informatics at Stanford University in the USA.

10:10 – 10:40am

1-2-1 Meetings / Networking Break

10:40 – 11:10am

1-2-1 Meetings / Networking Break

11:10 – 11:20am

Morning Refreshments

11:20am – 12:20pm

Defining Data Quality and How It Can be Achieved and Maintained at Scale

How do we assess and test the quality of a dataset? What threshold does it need to hit in order to be included in training an AI/ML model?
How do we verify possible dataset challenges (e.g. outliers) at scale? Is it even possible beyond a handful of data points?
What do you think about the peer review process to be brought down to the data world? Is there is a parallel in that world, and if not can there be one? Should we be adopting dataset standards that already exist?

Norman Azoulay

Director, Scientific Products
Excelra

Norman comes with a rich product management background that includes experience in developing and launching enterprise solutions. He brings a user centric and analytically driven approach; the ability to identify areas of growth and opportunity; and experience in setting strategic goals that align with the business objectives of the organization. Norman is leading Excelra’s rapid foray into technology solutions to accelerate innovation in drug development. He holds a Master of Liberal Arts, Extension Studies with a focus in Biology, from Harvard University.

Noor Shaker

Founder and Chief Executive Officer
Glamorous AI

Noor is a serial biotech entrepreneur with a track record of achievements in AI having held an Assistant Professorship from Aalborg University. Noor has published numerous papers in the field of artificial intelligence and its application to drug discovery and is an inventor on a handful of patents. She is passionate about data and AI and on a mission to cure disease with the power of human and machine learning. She is currently the CEO at GlamorousAI, a biotech company that pushes the boundaries to what is possible with AI to cure debilitating diseases. She is a recognised healthcare leader, MIT innovator under 35 and in BBC 100 women for 2019.

12:20 – 1:20pm

Networking Lunch

1:20 – 1:50pm

Spotlight Presentation

Building drug discovery on stronger foundations: AI-Augmented target identification at BenevolentAI

BenevolentAI develops and applies AI to drug discovery to find treatments that are more likely to be successful in the clinic and effective in patients. In this talk, Bryn Williams-Jones, VP Drug Discovery, will explore how he bridges the world’s of science and technology to help guide technologists in their work to develop AI tools that meet hyper-specific scientific requirements. He will then demonstrate how BenevolentAI applies these tools to empower target identification, precision medicine and molecular design. Bryn will cite case studies from BenevolentAI’s work in novel target discovery, drug repurposing and in collaboration with AstraZeneca.

Bryn Williams-Jones

Vice President Drug Discovery
BenevolentAI

Bryn is responsible for driving the application of AI-augmented drug discovery to disease hypothesis generation across the BenevolentAI portfolio bringing together the key scientific and AI strands of research. Before joining BenevolentAI Bryn led the Open PHACTS IMI project, and is CEO of the Open PHACTS Foundation project building open semantic web infrastructure for drug discovery. Prior to that Bryn worked at Pfizer across a wide range of Therapeutic Areas applying data and computational science to early drug discovery hypothesis generation.

1:55 – 2:55pm

FAIRifiation of data in a real-life data warehouse in big pharma

Homogeneity and heterogeneity in pharmaceutical research data sources (public and corporate)
Practical aspects of FAIRification processes
Deployment of FAIRified data to bench scientists

Peter Monecke

Senior Scientist In-silico Toxicology
Sanofi

Organic chemist by education (PhD at University of Goettingen) and chemoinformatics scientist by heart. He combines 9 years of experience in Computer-Aided Drug Design with 8 years in leading the physicochemical wet-lab in Frankfurt. In 2020, he switched to Sanofi’s In-silico Toxicology group with main focus on workflow digitalisation of existing toxological processes and assessments.

This combination of drug design experience with deep insights in wet-lab processes and data management approaches was the foundation to FAIRify a significant part of Sanofi’s data warehouse, e.g. ADME properties, physicochemical properties, in-vivo PK and biological compound profile.
The strength of the FAIRification process was proven by using FAIRified solubility data to take part in the global “Solubility Challenge” contest of 2019, submitting predictions within Top3 of all contributors.

3:00 – 3:30pm

1-2-1 Meetings / Networking Break

3:30 – 4:00pm

1-2-1 Meetings / Networking Break

4:00 – 4:10pm

Afternoon Refreshments

4:10 – 4:40pm

Target Identification

Time

Titles and Bullets

Facilitator

8:00 – 8:30am

Registration

8:30 – 9:00am

Opening Address & Keynote Presentation

Closing the data-insight gap

The volume of biological data is growing exponentially
But analytic capabilities are not keeping up – broadening the data-insight gap
Computational disease models can help. They’re designed to process high throughput and support R&D in every phase of the drug lifecycle

Shai Shen-Orr

Co-founder and Chief Scientist
CytoReason

9:05 – 10:05am

Challenges and opportunities in public-private partnership to accelerate drug discovery in Europe

Public-Private Partnerships (PPP) Opportunities in EU member states
Public-Private Partnerships (PPP) Challenges in EU member states
Public-Private Partnerships (PPP): EU vs US
Recommendations for decision makers

Mohamed-Ramzi Temanni

Scientific Director, Head of France AI & Genomics
Janssen

Dr. Ramzi Temanni is a Bioinformatics Leader with 15 years of international experience working closely with biologists, clinicians, and scientists in academia, hospitals, and biotech/Pharma helping them analyze large-scale biomedical data and solve complex research questions.

He was actively involved in 3 European projects; He helped build bioinformatics and scientific computing capability within Sidra Research. In addition, he led the Bioinformatics analysis for the Qatar Genome Programme.

Since January 2021, he has led the French AI and Genomics team based in Paris within the Computational Sciences R&D team of The Janssen Pharmaceutical Companies of Johnson & Johnson. He focuses on developing open innovation in France by pursuing public-private partnership activities in Artificial Intelligence and Genomics.

10:10 – 10:40am

1-2-1 Meetings / Networking Break

10:40 – 11:10am

1-2-1 Meetings / Networking Break

11:10 – 11:20am

Morning Refreshments

11:20am – 12:20pm

Lead Generation

Time

Titles and Bullets

Facilitator

8:00 – 8:30am

Registration

8:30 – 9:00am

Opening Address & Keynote Presentation

Closing the data-insight gap

The volume of biological data is growing exponentially
But analytic capabilities are not keeping up – broadening the data-insight gap
Computational disease models can help. They’re designed to process high throughput and support R&D in every phase of the drug lifecycle

Shai Shen-Orr

Co-founder and Chief Scientist
CytoReason

9:05 – 10:05am

Exploring Tactics for AI/ML Application in Developing TPP (Target Product Profile)

Share obfuscated TPPs and the tactics of AI/ML application for developing the TPPs
Explore other AI tools or omics approach from different therapeutic areas to light a new path for your TPP development
What is the ideal combination of human interaction and AI for the development of TPPs?

Paraskevi Gkeka

Principal Scientist, Structure Design and Informatics, CADD
Sanofi

Paraskevi Gkeka (PG) is currently a Principal Scientist at Sanofi R&D France. She has an M.Eng. in Applied Mathematics from the National Technical University of Athens, Greece, and an M.Sc. in Mathematical Modeling from the same university. PG got her PhD in Chemical Engineering from the University of Edinburgh, UK, in 2010 under the supervision of Dr. Sarkisov. Her thesis research examined peptide-membrane and nanoparticle-membrane interactions through the use of state-of-the-art molecular dynamics simulations. She then joined Dr. Cournia’s lab at the Academy of Athens to perform post-doctoral studies in computer-aided drug design and molecular modeling. During this time, she employed different computational methods for the design of novel therapeutic agents for multiple disease-related targets. Since 2016, she works at Sanofi as a principal scientist in the department of Integrated Drug Discovery. She is currently contributing to the development of AI-driven enhanced sampling algorithms to overcome the time-scale problem of molecular simulations and compute free energy surfaces. Her ultimate goal is to revolutionize drug discovery and patient treatment planning based on AI combined with conventional drug design methods.

10:10 – 10:40am

1-2-1 Meetings / Networking Break

10:40 – 11:10am

1-2-1 Meetings / Networking Break

11:10 – 11:20am

Morning Refreshments

11:20am – 12:20pm

The case for scientific portfolio management

Mechanism is king in drug development. Understanding the competition is queen.
At any given time, different users in the organization need different scientific insights.
What if there were one platform that could satisfy everyone’s needs and deliver multiple use cases?

Shai Shen-Orr

Co-founder and Chief Scientist
CytoReason

Professor Shai Shen-Orr is the Co-founder and Chief Scientist of CytoReason, a technology company developing computational disease models. He serves as Associate Professor in the Faculty of Medicine at the Technion—where he directs the laboratory of Systems Immunology and Precision Medicine. In his research, Prof. Shen-Orr develops new analytical methodologies for grappling with the intricate complexities of the immune system, especially as they occur in advanced age, and for defining biomarkers to evaluate immune health. His work has been featured and cited in numerous top-tier journals and systems biology textbooks, and has laid the foundation for CytoReason’s disease models. To date, six of the world’s top ten pharma companies use CytoReason’s technology to speed up drug discovery and dramatically cut the cost of developing new medicines.

12:20 – 1:20pm

Networking Lunch

1:20 – 1:50pm

Spotlight Presentation

Building drug discovery on stronger foundations: AI-Augmented target identification at BenevolentAI

Bryn Williams-Jones

Vice President Drug Discovery
BenevolentAI

1:55 – 2:55pm

Creating Viable Proof of Concept Molecules with AI/ML to Initiate Lead Generation

Discuss how AI can be effectively applied for the development of lead generation models using internal and external data
Explore the current AI capabilities in predicting drug-like properties
How much should you trust your model? How can you mitigate and plan for uncertainties in the model?

Prakash Rathi

Lead AI engineer (Augmented Drug Design Platform)
AstraZeneca

Prakash finished his PhD in computational biotechnology from University of Dusseldorf in 2014. He then worked at Astex Pharmaceuticals, Cambridge, UK as a postdoc and a scientific software developer working and development of deep learning models and tools for early drug discovery. Since Dec 2019, Prakash is technical lead for AI/ML workstream within the Augmented drug design platform at AstraZeneca leading multiple workstreams. He has over ten years of experience of developing scientific software and machine leaning models for drug design.

3:00 – 3:30pm

1-2-1 Meetings / Networking Break

3:30 – 4:00pm

1-2-1 Meetings / Networking Break

4:00 – 4:10pm

Afternoon Refreshments

4:45 – 5:45pm

Augmented intelligence in leads screening and selection

How can AI/ML be applied for the development and selection of screening libraries and assays and are we there yet for novel, large-scale screening technologies
What AI approaches do work for virtual screening outcomes and Hit ID and what areas to focus on
Discuss how to leverage AI/ML to optimize lead identification and selection in data poor settings such as in-vivo safety and efficacy

Nikolaus Stiefl

Associate Director Data Science, GDC CADD
Novartis

Nik is a data scientist with a passion for data and early drug discovery. Trained as a pharmacist, Nik switched to chemoinformatics, machine learning and molecular modelling early in his career. As part of multiple small-molecule drug discovery projects he delivered a range of discovery candidates. He also developed new scientific algorithms as well as software for the community.
Since 2019, Nik is the product owner of two major digital initiatives at NIBR – one more focusing on machine learning and the other on data science and visualization. On a strategic level, Nik shapes NIBR’s digital future as a core team member of the Computational Sciences Council as well as a member of the digital NIBR Leadership Team and the extended Leadership Team of the AI Innovation Center.
Outside work, Nik enjoys family life, is an avid fisherman and enjoys all kinds of other outdoor activities as well as fixing and building things (of mostly any type).

5:45 – 6:45pm

Evening Drinks Reception

Lead Optimization

Time

Titles and Bullets

Facilitator

8:00 – 8:30am

Registration

8:30 – 9:00am

Opening Address & Keynote Presentation

Closing the data-insight gap

The volume of biological data is growing exponentially
But analytic capabilities are not keeping up – broadening the data-insight gap
Computational disease models can help. They’re designed to process high throughput and support R&D in every phase of the drug lifecycle

Shai Shen-Orr

Co-founder and Chief Scientist
CytoReason

9:05 – 10:05am

Accelerating the Optimization of Small Molecules Using AI/ ML-Led Approaches

Explore application of AI in speeding drug-design-test-analysis cycle
What are the current successes and challenges in AI-led small molecules optimization?
How effective are AI at suggesting novel, synthesizable compound design?
Thinking of ADMET at the stage of compound design
Retrosynthesis to suggest more efficient path to make compounds

Aniket Magarkar

Principal Scientist, Computational Chemistry
Boehringer Ingelheim

Molecular modeler by education (Ph.D. at the University of Helsinki) and data scientist in practice. I have over 9 years of experience in molecular modeling and 4 years of research experience in CADD. I am part of the Data Science team in Computational Chemistry at Boehringer Ingelheim. I build automated tools and workflows that can aid drug designers in small molecule research, in particular, lead identification and lead optimization with machine learning and/or 3D methods.

10:10 – 10:40am

1-2-1 Meetings / Networking Break

10:40 – 11:10am

1-2-1 Meetings / Networking Break

11:10 – 11:20am

Morning Refreshments

11:20am – 12:20pm

Accelerating DMTA drug discovery cycle using machine learning based CASP

How Is CASP currently used in the pharmaceutical industry?
What is the status and barriers for adoption by users?
How can we, as a research community, make CASP better?
View on importance of in-house data for predictive models; status of ML-readiness of ELN data (quality of ELN data); willingness / interest in sharing negative data in a pre-competitive environment

Ivan Krstic

Senior Director of Product Management
Reaxys (Elsevier)

Ivan Krstic is Senior Director of Product Management at Elsevier. His team is responsible for Reaxys, comprehensive and innovative chemistry research information system that combines reactions, substance and bioactivity data with cheminformatics and machine learning to support research and digitalization in pharma. Ivan holds PhD in chemistry from Goethe University in Germany.

12:20 – 1:20pm

Network Lunch

1:20 – 1:50pm

Spotlight Presentation

Building drug discovery on stronger foundations: AI-Augmented target identification at BenevolentAI

Bryn Williams-Jones

Vice President Drug Discovery
BenevolentAI

1:20 – 1:50pm

Lead Generation topic:
Are AI-based protein structure predictions accelerating drug discovery?

Scenarios where such structure predictions may be on a par with experimentally determined structures
Limitations of AI-informed structure predications
Current impact of AI-based protein structure predictions for drug discovery and future prospects

Zara Sands

Head of CADD & Informatics
Confo Therapeutics

Zara joined Confo Therapeutics as Head of Computer-Aided Drug Discovery in 2020 and shortly thereafter was also appointed as Head of Informatics.
Zara has over 14 years of drug discovery experience. Prior to joining Confo Therapeutics, Zara worked at AstraZeneca where she applied cutting edge in silico approaches to support small molecule and biologic design activities against challenging neuroscience targets. In 2009, she was recruited by UCB BioPharma to develop and apply state-of-the-art computational approaches to enhance its CNS drug design and target assessment activities. Zara was instrumental in developing UCB’s membrane protein drug discovery capabilities, playing a key role in the design of GPCR clinical candidates.
Zara received a PhD in Computational Chemistry from the University of Nottingham (UK). Subsequently, she conducted postdoctoral research, gaining expertise in computational structural biology and molecular biophysics of membrane proteins at the University of Oxford (UK) in the Group of Prof. Mark Sansom.

3:00 – 3:30pm

1-2-1 Meetings / Networking Break

3:30 – 4:00pm

1-2-1 Meetings / Networking Break

4:00 – 4:10pm

Afternoon Refreshments

4:10 – 4:40pm

Drug Response Prediction

Time

Titles and Bullets

Facilitator

8:00 – 8:30am

Registration

8:30 – 9:00am

Opening Address & Keynote Presentation

Closing the data-insight gap

The volume of biological data is growing exponentially
But analytic capabilities are not keeping up – broadening the data-insight gap
Computational disease models can help. They’re designed to process high throughput and support R&D in every phase of the drug lifecycle

Shai Shen-Orr

Co-founder and Chief Scientist
CytoReason

9:05 – 10:05am

1-2-1 Meetings

9:05 – 10:05am

ADMET Simulation and Drug Dosage Prediction Using AI/ ML

What is the promise and limitation of current approaches to simulate ADMET in silico?
Which aspects of ADMET can be currently modeled mechanistically vs. empirically vs. neither?
What types of experiments or data would help to address blind spots in our understanding and ability to predict ADMET?
How can AI/ML narrow the translation gap between preclinical models and human?

Giuseppe Marco Randazzo

Head of AI and Cheminformatics
Endogena Therapeutics

Giuseppe Marco Randazzo is a cheminformatic data scientist with more than 10+ experience in drug discovery. After completing his Ph.D. in chemical science at the laboratory of chemometrics and cheminformatic University of Perugia, in 2013 moved to the pharmaceutical sciences department of the University of Geneva for a post-doc position in the analytical chemistry group. He worked on machine-learning (ML)/cheminformatics tools for automatic compound annotation in metabolomics approaches. He also worked in a joint venture between the University of Geneva and a private industrial partner, Firmenich, delivering new active nature-identical compounds using ML and cheminformatics approaches. In 2017 he joined the Dalle Molle Institute for Artificial Intelligence in Lugano, developing new AI tools for drug discovery. In 2019 he joined F. Hoffmann-La Roche as a post-doc scientist to implement new machine-learning and AI methods for polypharmacology. In 2021 he joined Endogena Therapeutics in the role of Head of AI and Cheminformatics. He is an enthusiast and active open-source supporter, contributing to several scientific projects.

10:10 – 10:40am

1-2-1 Meetings / Networking Break

10:40 – 11:10am

1-2-1 Meetings / Networking Break

11:10 – 11:20am

Morning Refreshments

11:20am – 12:20pm

Augmented Intelligence in Drug Discovery Xchange
Europe 2021, December 3rd

Data Quality

Target Identification

Lead Generation

Lead Optimization

Drug Response Prediction

Lead Partner

Partners

© Copyright 2023 by Weblify & Webtec

Augmented Intelligence in Drug Discovery XchangeEurope 2021, December 3rd

Data Quality

Target Identification

Lead Generation

Lead Optimization

Drug Response Prediction

Lead Partner

Partners

Augmented Intelligence in Drug Discovery Xchange
Europe 2021, December 3rd