We’re pleased to announce that a new themed collection from Digital Discovery has now been published online.
This collection features articles from contributors to the 2nd Workshop on AI in Drug Discovery, held within the esteemed 34th International Conference on Artificial Neural Networks (ICANN 2025). The workshop brought together machine learning experts, computational chemists and chemoinformaticians working on the development and application of ML in chemistry, environmental health and (eco)toxicology.
This collection reflects cutting-edge contributions at the workshop in the rapidly evolving field of AI-driven drug discovery, encompassing facets such as generative models, eXplainable AI (XAI), uncertainty quantification, reaction informatics and synthetic route prediction, quantum machine learning for reactivity, methodologies for mining very large compound data sets, federated learning, analysis of HTS data, multimodal and equivariant neural networks, and other topics related to the use of ML in chemistry.
The line-up of articles has been provided below. All articles in Digital Discovery are open access and free to read.
Editorial
Introduction to ”AI in Drug Discovery at ICANN2025”
Igor V. Tetko and Djork-Arné Clevert
Digital Discovery, DOI: 10.1039/D6DD90045C
Papers
Interpreting graph neural networks with Myerson values for cheminformatics approaches
Samuel K. R. Homberg, Malte L. Modlich, Marlon Becker, Janosch Menke, Garrett M. Morris, Benjamin Risse and Oliver Koch
Digital Discovery, 2026, 5, 3378–3389, DOI: 10.1039/d5dd00577a
Benchmarking explainable AI methods for toxicophore detection and toxicity prediction
Dina Khasanova and Igor V. Tetko
Digital Discovery, 2026, 5, 2547–2559, DOI: 10.1039/d5dd00576k
MolEncoder: towards optimal masked language modeling for molecules
Fabian P. Krüger, Nicklas Österbacka, Mikhail Kabeshov, Ola Engkvist and Igor Tetko
Digital Discovery, 2025, 4., 3552–3566, DOI: 10.1039/d5dd00369e
MARCUS: molecular annotation and recognition for curating unravelled structures
Kohulan Rajan, Viktor Weißenborn, Laurin Lederer, Achim Zielesny and Christoph Steinbeck
Digital Discovery, 2025, 4, 3137–3148, DOI: 10.1039/d5dd00313j
We hope you enjoy this new themed collection from Digital Discovery