DrugSpace Symposium Spring 2023

DrugSpace
2023 Machine Learning ●
Artificial Intelligence ●
Neural Networks ●
Big Data ●
 A Network of
Possibilities A Network of Possibilities

Connect the Dots for Future Drug Discovery

In our third virtual BioSolveIT DrugSpace Symposium, focus is placed on the recent trends that transform modern drug discovery: machine learning, artificial intelligence, neural networks, and processing big data.
Can recent developments live up to the hype around AI, or is there still a long way to go? This event brings researchers together who aim to discover future drug candidates from a myriad of data flows. An exciting journey lies ahead of us!

Medicinal chemists, decision makers, representatives of crop-, pharmaceutical-, and medicine-related businesses, undergraduates and PhD students, researchers — simply anyone interested in future technologies and state-of-the-art drug development — you are cordially invited to participate in this virtual event. Again, this BioSolveIT Symposium aims to be accessible to the entire global research community; BioSolveIT takes pride in thanking all brilliant speakers and participants for their contribution to the event in advance.

The third DrugSpace Symposium takes place on 24 and 25th May, 2023 — starting daily at 3 pm CEST/Berlin. Registration and participation is free-of-charge.

Register for free for the DrugSpace 2023 Symposium

DrugSpace 2023 Programme

Once again, we are very proud to host renowned experts in their fields from around the globe, contributing their knowledge to the scientific community.
DrugSpace 2023 Programme (PDF)

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Share information, early announcements, publications, success stories, and anything else related to huge chemistry spaces and databases. Become a member, and feel free to contribute.
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Confirmed Speakers

  • Philippe Schwaller
    (École Polytechnique Fédérale de Lausanne)
    "AI-Accelerated Organic Synthesis"
  • Quentin Perron
    (Iktos)
    "Yes, You Should Use AI for Medicinal Chemistry"
  • Léa El Khoury
    (Qubit)
    "Application of Absolute Binding Free Energy Calculations to Predict the Binding Modes and Affinities of Protein-Protein Inhibitors"
  • Francesca Grisoni
    (Eindhoven University of Technology)
    "Deep Learning for Drug Discovery: Challenges and Opportunities"
  • Marcus Gastreich
    (BioSolveIT)
    "Claw Machines for Exploding Chemical Spaces"
  • Yurri Moroz
    (Chemspace)
    "Making Virtual REAL: Creation and Use of the Giga-Scale Chemical Spaces"
  • Dusan Petrovic
    (Nuvisan)
    "Virtual Screening for Multiple Modalities"
  • Connor Coley
    (Massachusetts Institute of Technology)
    "Learning to Navigate Synthetically Accessible Chemical Space"
  • Henry van den Bedem
    (Atomwise)
    "An Efficient Graph Generative Model for Navigating Ultra-Large Combinatorial Synthesis Libraries"
  • Nick Antonopoulos
    (DeepLab)
    "Scalable and High-Throughput Deep Neural Virtual Screening"
  • Lewis Martin
    (OpenBench)
    "Fast and Economical Hit Finding with Active Learning"
  • Daniel Kuhn
    (Merck)
    "You Can't Improve What You Don't Measure — Measuring ML/AI Impact in Drug Discovery Projects"
  • Christoph Grebner
    (Sanofi)
    "AI-Driven Mining of Accessible Chemical Spaces"

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