AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
EUROPE 2022
Zurich, November 25
Welcome to hubXchange’s Europe Augmented Intelligence (AI) in Drug Discovery Xchange 2022, 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 an In-Person meeting.
VENUE DETAILS: Hilton Zurich Airport Hotel, Hohenbuehlstrasse 10 – 8152 Opfikon, Switzerland
Data Quality
Opening Address & Keynote Presentation
X–Chem’s ArtemisAI Platform: a flexible approach to AI for drug discovery
- Intro to X-Chem
- ArtemisAI: X-Chem’s AI platform for DEL and beyond
- Overview of ArtemisAI case studies
Senior Vice President & General Manager, X-Chem
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 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 a recognised healthcare leader, MIT innovator under 35 and in BBC 100 women.
Vice President, Scientific Computing and Data Science, X-Chem
Marie-Aude has dedicated her career to the application of informatics in the field of drug discovery. At X-Chem, she has led a collaborative approach to generating high-quality data management solutions and a world-leading integrated suite of software tools to support X-Chem’s DNA-encoded library (DEL) platform. Having been part of X-Chem’s groundbreaking forays into machine learning on DEL, she now plays a key role in the continual expansion of X-Chem’s AI platform.
Garbage in, garbage out – curating data for activity / property prediction in drug discovery
- Internal databases not standardized (typos, negligence, delays, manual curation, historical artefacts, …)
- Extreme data imbalance
- Automated solutions hard to coordinate with multiple stakeholders involved
- Multidisciplinary expertise required
Senior Data Scientist ML/AI, Janssen Pharmaceuticals
Maciej Kanduła is a Senior Scientist at Janssen, designing, developing, and deploying AI-driven pipelines for finding potential drug candidates, and serving as an AI/ML lead in discovery programs. He focuses on enabling image-based compound activity prediction, supporting Janssen’s small molecule portfolio; and integrating information from heterogeneous data sources, at the input level—combining data modalities, and the desired output—fusing property data from databases.
Prior to joining Janssen, Maciej worked at the Institute for ML at the JKU Linz (Austria), consulted at the IARAI Institute in Vienna (Austria), and was a visiting scholar at the Boston University (US). Maciej holds a Ph.D. in Bioinformatics and contributed to multiple peer-reviewed publications.
Data quality and integrity: how do we achieved it?
- What approaches do you use to ensure and maintain data quality?
- Do you have a recurring process to confirm the relevancy if data through its lifetime?
- How can we get access to shared data?
- How does data quality affect your day-to-day efficiency?
- How do you improve data quality over time, balance that with the cost of generating data, and ensure that you are collecting informative rather than redundant data?
Senior Vice President & General Manager, X-Chem
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 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 a recognised healthcare leader, MIT innovator under 35 and in BBC 100 women.
Vice President, Scientific Computing and Data Science, X-Chem
Marie-Aude has dedicated her career to the application of informatics in the field of drug discovery. At X-Chem, she has led a collaborative approach to generating high-quality data management solutions and a world-leading integrated suite of software tools to support X-Chem’s DNA-encoded library (DEL) platform. Having been part of X-Chem’s groundbreaking forays into machine learning on DEL, she now plays a key role in the continual expansion of X-Chem’s AI platform.
Networking Lunch
1-2-1 Meetings / Networking Break
1-2-1 Meetings / Networking Break
Afternoon Refreshments
How to ensure high data quality sourced externally, either as datasets or as cloud-based service?
- Is it beneficial to use a cloud data-provider service instead of integrating databases locally?
- How to address local integration of data obtained from diverse sources?
- How to deal with a large degree of uniqueness in OMICs “big-data”?
Director Bioinformatics, Hengrui European Biosciences
Victor Zharavin has 15 years of academia and biotech experience. Being a well-respected achiever in the computational and life-science fields, he worked at Biozentrum Basel, TUM, collaborated with major companies including Novartis, Medigene Therapeutics, Agilent and Bruker. He has also consulted with several organizations including Roswell Park Cancer Center in Buffalo, USA.
Victor has conducted in-depth research on integrating tools into in silico pipelines on HPC platforms such as Galaxy.EU infrastructure, automation of cancer genetic diagnostics and therapy recommendations for Freiburg uniclinic/DKFZ, development of anti-cancer vaccines and prediction of potential side effects of immunotherapies.
