AI in Drug Discovery
First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings
Seiten
2024
Springer International Publishing (Verlag)
978-3-031-72380-3 (ISBN)
Springer International Publishing (Verlag)
978-3-031-72380-3 (ISBN)
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.
Erscheinungsdatum | 27.09.2024 |
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Reihe/Serie | Lecture Notes in Computer Science |
Zusatzinfo | XXXVIII, 176 p. 50 illus., 49 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Naturwissenschaften ► Chemie ► Physikalische Chemie | |
Schlagworte | Active learning • Big Data • chemo-informatics • constraints • convergent routes • convolution neural networks toxicity • Deep learning • de novo molecular design • drug discovery • equivariant graph neural networks • Explainable AI • feature decomposition • GNNS • molecular property prediction • open access • quantum-mechanical properties • solvent effects • Structure-Based Drug Discovery • Synthesis Planning • Transformers |
ISBN-10 | 3-031-72380-5 / 3031723805 |
ISBN-13 | 978-3-031-72380-3 / 9783031723803 |
Zustand | Neuware |
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