Paper
MARCUS: molecular annotation and recognition for curating unravelled structures
MARCUS: an open-source web platform that automates chemical literature curation, integrating text annotation with GPT-4 with 3 OCSR models. Human-in-the-loop for natural product research, streamlining data extraction to COCONUT database submission.
Paper
MolEncoder: towards optimal masked language modeling for molecules
Predicting molecular properties is a key challenge in drug discovery.
About this collection
We are pleased to share this themed collection of 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.
New articles will be added to this collection as they are published.