The main focus area of our workshop will be on applications of machine learning in pharmaceutical and healthcare settings. It will be a platform for researchers working in industry and academia on various applications of machine learning in these fields.
This is the 6th. iteration of "PharML" at ECML-PKDD. Dive into the history of our past meetings (2020, 2021, 2022, 2023, 2025) and join us this year as we continue to push the boundaries of innovation and discovery in machine learning for pharm and healthcare.
Large Language Models and Transformers for Pharma
Scientific knowledge extraction
Assisted indication selection
Assisted Medical Writing
Therapeutic antibody and protein representations
Single cell transcriptome representations
Learning on Graphs
Node and link prediction on molecular graphs
Modelling dynamic graphs
Biological pathway mining for cancer biomarker discovery
Representing patients and molecules as graphs
Causal inference and learning
Predicting the impact of interventions and discovering causal relationships from observational data.
Machine learning for survival analysis
Methods for learning from time-to-event data to build prognostic or treatment-predictive models.
Multimodal and data fusion
Leveraging multiple modalities including images, molecular data, text, and sensor data.
Applied machine-learning
Drug Design and Discovery
Translational Medicine
Earlier Diagnosis and Differential Diagnosis
Personalized Healthcare and Precision Medicine
Patient Safety, Pharmacovigilance, and Toxicity Prediction
Patient Monitoring and Diagnostic Surveillance
Medical Devices and Digital Endpoints
Clinical Decision Support
Cluster Analysis of Electronic Health Records
Medical Image Processing
Electrogram Biosignal Processing
Submission Deadline: June 5, 2026 (AoE)
Notification: June 12, 2026
Camera-read Submission: July 10, 2026
Workshop date: To be confirmed
We invite authors to submit their work in either an abstract or extended format.
Research abstracts: describe preliminary results and are meant to foster discussion of emerging topics. Maximum of 6 pages.
Long papers: present mature work, published or unpublished. Maximum of 16 pages.
Authors of accepted papers will be notified whether their paper is selected for oral presentation or poster session.
All manuscripts will be peer-reviewed and the review process is strictly double-blind.
All submissions must be anonymous. Make sure the document(s) you upload does not contain names, affiliations, acknowledgements or any information that can be used to directly identify you. Replace the author information with "Anonymous". Avoid including URLs of GitHub repositories linked to you.
Manuscripts should be in PDF format, following the Springer Lecture Notes in Computer Science style (See link for LaTeX template).
Submissions should be done via the Conference Management Toolkit (CMT) page (link). Follow these steps to submit your manuscript:
In the “Submissions” tab, make sure the selected conference is “ECMLPKDDWorkshopTrack2026”.
When you click the button “+ Create new submission”, select “PharML, Machine Learning for Pharma and Healthcare Applications” as the track.
The PharML workshop will partner again this year with Springer to publish the workshop proceedings (See for example, link to 2023 proceedings)
Only unpublished work or extended versions of the abstracts are eligible for publication in the workshop proceedings.
Moreover, we plan to invite the authors of all the accepted short papers to submit an extended version of their articles in a supplement of a journal dedicated to our workshop.
Important note from the Conference Organizers: accepted manuscripts at the workshop without a full registration of one of the authors or in-presence presentation will not be included in the workshop proceedings.