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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| Format: | Online |
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| Language: | English |
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Springer Nature
2024
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| Online Access: | ONIX_20241021_9783031723810_28 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-146360 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1463602024-10-23T04:04:49Z AI in Drug Discovery Clevert, Djork-Arné Wand, Michael Malinovská, Kristína Schmidhuber, Jürgen Tetko, Igor V. 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 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. 2024-10-23T04:04:48Z 2024-10-23T04:04:48Z 2024-10-21T15:27:03Z 2025 book ONIX_20241021_9783031723810_28 https://library.oapen.org/handle/20.500.12657/93866 9783031723810 9783031723803 https://directory.doabooks.org/handle/20.500.12854/146360 eng Lecture Notes in Computer Science open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/93866/1/978-3-031-72381-0.pdf Springer Nature Springer Nature Switzerland 10.1007/978-3-031-72381-0 10.1007/978-3-031-72381-0 9fa3421d-f917-4153-b9ab-fc337c396b5a 00d1f756-909d-4cbf-8eb4-51cca261bca3 9783031723810 9783031723803 Springer Nature Switzerland 176 Cham [...] open access |
| spellingShingle | 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 AI in Drug Discovery |
| title | AI in Drug Discovery |
| title_full | AI in Drug Discovery |
| title_fullStr | AI in Drug Discovery |
| title_full_unstemmed | AI in Drug Discovery |
| title_short | AI in Drug Discovery |
| title_sort | ai in drug discovery |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20241021_9783031723810_28 |