AI in Drug Discovery : First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings

doi:10.1007/978-3-031-72381-0

Contributor(s)

Clevert, Djork-Arné (editor)

Wand, Michael (editor)

Malinovská, Kristína (editor)

Schmidhuber, Jürgen (editor)

Tetko, Igor V. (editor)

Language

English

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Abstract

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.

URI

https://doab-dev.siscern.org/handle/20.500.12854/160751

Keywords

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

DOI

10.1007/978-3-031-72381-0

ISBN

9783031723810, 9783031723803

Publisher

Springer Nature

Publisher website

http://www.springernature.com/oabooks

Publication date and place

Cham, 2025

Imprint

Springer Nature Switzerland

Series

Lecture Notes in Computer Science,