The 3rd Workshop on AI in Drug Discovery (the 3rd “AIDD” Workshop, https://e-nns.org/icann2026/the-3rd-workshop-on-ai-in-drug-discovery/) will be held in Padua, Italy as part of the 35th International Conference on Artificial Neural Networks (ICANN 2026). This workshop seeks cutting-edge contributions in the rapidly evolving field of AI-driven drug discovery. We invite submissions on a broad range of topics at the intersection of machine learning and drug discovery, including generative models, eXplainable AI (XAI), uncertainty quantification, reaction informatics and synthetic route prediction, quantum machine learning for reactivity, methodologies for mining large compound data sets, federated learning, analysis of HTS data, multimodal and equivariant neural networks, foundation models, LLM and Agentic AI applications in chemistry and life sciences, co-folding (incl. protein structure prediction and protein design) models and other topics related to the use of machine learning (ML) in chemistry. This workshop aims to bring together machine learning experts, computational chemists and chemoinformaticians working on the development and application of ML in chemistry, environmental health and (eco)toxicology.
Programme
The 3rd AIDD Workshop will take place in the afternoon on Monday, the 14th of September 2026 - the first day of the ICANN 2026 conference at Congress Centre A. The first session will commence at 14:30 CEST, after which there will be a short break of ~20 minutes. The second session will start at 16:30 CEST.
14:30 Accurate ΔTm Prediction Without Protein Structure Inputs for Biomolecular Stability, Mario Wieser (Genedata AG)
14:50 Fuzzy Atom Guidance for Ligand Generation, Joel Nicholls (SyntheticGestalt)
15:10 A physically interpretable symbolic language of molecular recognition, Emanuele Criscuolo (TU Eindhoven)
15:30 ELF Graphs: Beyond Atoms and Bonds for Molecular Property Prediction, Subashini Kennedy (Sanofi)
15:50 GraphVAEBM: Modular Energy‑Guided Graph Generation, Christian Mancini (Università degli Studi di Firenze)
16:10 Coffee Break
16:30 Optimization of parallel synthesis conditions with machine learning methods, Fabrizio Ambrogi (Selvita)
16:50 Mini Chemical Llama based Model for Compound Toxicity Prediction, Abraham Yosipof (College of Law & Business, Ramat-Gan)
17:10 Compound-target Networks improve Protein Sensitivity Prediction over PPI networks, Alessandro Dipalma (University of Pisa)
17:30 Data Quality Matters: Improving Reaction Prediction via USPTO Database Curation, Ferruccio Palazzesi (Evotec)
17:50 Peptides Encoding for Machine Learning, Hanoch Senderowitz, Bar-Ilan University
The authors of articles/abstracts presented at the 3rd AIDD workshop are invited to submit their full articles to the special issue of J. Cheminformatics by the 31th of December 2026 and will get 25% of discount to publish their article in the journal. This workshop is partially supported by the Marie Skłodowska-Curie Actions (MSCA) Doctoral Network European Industrial Doctorate “Explainable AI for Molecules” (AiChemist https://aichemist.eu).
Organizers:
Igor V. Tetko, Helmholtz Munich and BIGCHEM GmbH, Germany
Ola Engkvist, AstraZeneca, Sweden
Matteo Aldeghi, Bayer, USA
Djork-Arné Clevert, Pfizer Worldwide Research Development and Medical, Berlin, Germany
Marc Bianciotto, Sanofi, Paris, France
Katya Ahmad, Helmholtz Munich, Germany
Program Commitee
All PIs of the project, see Partners