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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 / Djork-Arn�e Clevert, Michael Wand, Krist�ina Malinovsk�a, J�urgen Schmidhuber, Igor V. Tetko, editors.

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 14894.Publisher: Cham : Springer, [2025]Description: 1 online resource (xxxviii, 176 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031723810
  • 3031723813
Other title:
  • AIDD 2024
Subject(s): DDC classification:
  • 615.1/90028563 23/eng/20241106
LOC classification:
  • RM301.25
Online resources: Summary: 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.
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Open access. GW5XE

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.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed September 23, 2024).

Some versions: Open access versions available from some providers

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