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Start Over Descriptor "De novo molecular design" Publisher springer nature switzerland
2 results on '"De novo molecular design"'

1. Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery

Author

Nahal, Yasmine, Heinonen, Markus, Kabeshov, Mikhail, Janet, Jon Paul, Nittinger, Eva, Engkvist, Ola, Kaski, Samuel, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Clevert, Djork-Arné, editor, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2025
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2. AI in Drug Discovery

Author

Clevert, Djork-Arné, Wand, Michael, Malinovská, Kristína, Schmidhuber, Jürgen, and Tetko, Igor V.

Subjects
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
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.

Published
2025
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