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AI in Drug Discovery

First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings

  • Conference proceedings
  • Open Access
  • Oct 2024

You have full access to this open access Conference proceedings

Overview

Editors:
  1. Djork-Arné Clevert

    1. Machine Learning Research, Pfizer (Germany), Berlin, Germany

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  2. Michael Wand

    1. IDSIA USI-SUPSI, Viganello, Switzerland

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  3. Kristína Malinovská

    1. Comenius University, Bratislava, Slovakia

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  4. Jürgen Schmidhuber

    1. IDSIA USI-SUPSI, Viganello, Switzerland

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  5. Igor V. Tetko

    1. Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Munich, Germany

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  • This book is open access, which means that you have free and unlimited access

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14894)

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About this book

This open Access book constitutes the refereed proceedings of the First International Workshop on AI in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024.

The 12 papers presented here were carefully reviewed and selected for these open access proceedings. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.

Keywords

  • Open Access
  • Synthesis planning
  • chemo-informatics
  • big data
  • deep learning
  • drug discovery
  • convolution neural networks toxicity
  • GNNs
  • transformers
  • explainable AI
  • active learning
  • feature decomposition
  • de novo molecular design
  • quantum-mechanical properties
  • solvent effects
  • molecular property prediction
  • convergent routes
  • equivariant graph neural networks
  • structure-based drug discovery
  • constraints

Editors and Affiliations

  • Machine Learning Research, Pfizer (Germany), Berlin, Germany

    Djork-Arné Clevert

  • IDSIA USI-SUPSI, Viganello, Switzerland

    Michael Wand, Jürgen Schmidhuber

  • Comenius University, Bratislava, Slovakia

    Kristína Malinovská

  • Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Munich, Germany

    Igor V. Tetko

Bibliographic Information

  • Book Title: AI in Drug Discovery

  • Book Subtitle: First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings

  • Editors: Djork-Arné Clevert, Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko

  • Series Title: Lecture Notes in Computer Science

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2025

  • Softcover ISBN: 978-3-031-72380-3Due: 05 November 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXIX, 173

  • Number of Illustrations: 46 b/w illustrations

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