AI in Drug Discovery
First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings
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Web ShopContributor(s)
Clevert, Djork-Arné (editor)
Wand, Michael (editor)
Malinovská, Kristína (editor)
Schmidhuber, Jürgen (editor)
Tetko, Igor V. (editor)
Language
EnglishAbstract
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
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; constraintsDOI
10.1007/978-3-031-72381-0ISBN
9783031723810, 9783031723803, 9783031723810Publisher
Springer NaturePublisher website
https://www.springernature.com/gp/products/booksPublication date and place
Cham, 2025Imprint
Springer Nature SwitzerlandSeries
Lecture Notes in Computer Science, 14894Classification
Artificial intelligence
Data mining
Expert systems / knowledge-based systems
Computational chemistry