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Published by Springer International Publishing AG, Cham, 2024
ISBN 10: 3031723805 ISBN 13: 9783031723803
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Paperback. Condition: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
paperback. Condition: Good. Book is slightly bending/warping. Cover shows minor creasing. Pages are clean, text and pictures are intact and unmarred. Good reading copy.
Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Language: English
Published by Springer Nature Switzerland Okt 2024, 2024
ISBN 10: 3031723805 ISBN 13: 9783031723803
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open Access book constitutes the refereed proceedings of theFirst International Workshop onAI in Drug Discovery, AIDD 2024, held as a part of the33rd 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, includingBig 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.