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AI in Drug Discovery

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 her...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: Springer Nature 2024
Matèries:
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
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems
thema EDItEUR::P Mathematics and Science::PN Chemistry::PNR Physical chemistry::PNRA Computational chemistry
Accés en línia:ONIX_20241021_9783031723810_28
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Sumari: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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