My Account Log in

5 options

AI in Drug Discovery : First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings / edited by Djork-Arné Clevert, Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko.

DOAB Directory of Open Access Books Available online

View online

OAPEN Available online

View online

Springer Nature - Springer Computer Science (R0) eBooks 2025 English International Available online

View online

Springer Nature - Springer Nature Link Journals and eBooks - Fully Open Access Available online

View online

SpringerLink Open Access eBooks Available online

View online
Format:
Book
Contributor:
Clevert, Djork-Arné., Editor.
Wand, Michael., Editor.
Malinovská, Kristína., Editor.
Schmidhuber, Jürgen, Editor.
Tetko, Igor V., Editor.
Series:
Lecture Notes in Computer Science, 1611-3349 ; 14894
Language:
English
Subjects (All):
Artificial intelligence.
Data mining.
Chemistry--Data processing.
Chemistry.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Computational Chemistry.
Local Subjects:
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Computational Chemistry.
Physical Description:
1 online resource (XXXVIII, 176 p. 50 illus., 49 illus. in color.)
Edition:
1st ed. 2025.
Place of Publication:
Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Summary:
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.
ISBN:
9783031723810
3031723813

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

Find

Home Release notes

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Find catalog Using Articles+ Using your account