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AI in Drug Discovery
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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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Keywords
- Active learning
- big data
- chemo-informatics
- constraints
- convergent routes
- convolution neural networks toxicity
- de novo molecular design
- deep learning
- Drug Discovery
- equivariant graph neural networks
- explainable AI
- feature decomposition
- GNNs
- molecular property prediction
- quantum-mechanical properties
- solvent effects
- structure-based drug discovery
- Synthesis planning
- thema EDItEUR::P Mathematics and Science::PN Chemistry::PNR Physical chemistry::PNRA Computational chemistry
- 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
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems
- transformers
Links
DOI: 10.1007/978-3-031-72381-0web: https://link.springer.com/book/10.1007/978-3-031-72381-0
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