International Workshop on
International Workshop on
Multi-Modal Artificial Intelligence in Drug Discovery, Repurposing, and Safety MAI-DRS 2026
Multi-Modal Artificial Intelligence in Drug Discovery, Repurposing, and Safety MAI-DRS 2026
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Call For Papers
Submission Deadline: September 20, 2026
Acceptance Notification: October 20, 2026
Camera-Ready Submission: November 8, 2026
Submission Guidelines:
Submission Link:
Submission Format: https://www.ieee.org/conferences/publishing/templates
We invite papers (upto 8 pages long) related to the application of multimodal AI in drug discovery, repurposing, and safety. Key topics include, but are not limited to,
Multimodal learning for drug discovery and design
AI-driven drug repurposing using multi-omics and clinical data
Integration of chemical, biological, and clinical modalities
Graph neural networks for drug-target and drug-disease interactions
Generative artificial intelligence in drug design.
Multi-Omics and Spatial Transcriptomics in Medicine.
Knowledge graphs for pharmacology and drug repurposing
Prediction of drug safety, toxicity, and adverse drug reactions
Integration of heterogeneous data sources for pharmacovigilance, with emphasis on normalization, weighting, and harmonization strategies
Statistical stability in pharmacovigilance overcoming the lack of exposure populations in incident-focused safety databases
Cross-modal representation learning in biomedicine
Explainable and trustworthy AI for pharmacological applications
AI for pharmacogenomics and personalized therapeutics
Benchmark datasets and evaluation frameworks for multimodal drug discovery
Workshop Style: Hybrid (onsite + online participation). The organizers will ensure availability and management of the virtual meeting platform to support remote participation, presentations, and discussions.
General Description of Workshop.
Recent advances in artificial intelligence (AI) have significantly accelerated drug discovery and development by leveraging diverse biomedical data sources, including omics data, chemical structures, biomedical literature, and clinical records. AI-driven pharmacovigilance leverages Electronic Health Records (EHR), spontaneous reporting systems, and patient-generated narratives on social media to enable more comprehensive detection of adverse drug reactions and emerging safety signals. Multimodal AI approaches enable the integration of these heterogeneous data types to improve target identification, function prediction, drug repurposing, and safety prediction. Moreover. Generative large language models are now being used widely in every aspect of the drug discovery process and drug safety by extracting medical entities and learning latent representation. Despite promising progress, challenges remain in data quality and availability; data heterogeneity and integration; explainability and interpretability; bias and trustworthiness; and evaluation, validation, and clinical translation.
This workshop aims to bring together researchers and practitioners from bioinformatics, machine learning, cheminformatics, and clinical sciences to discuss cutting-edge methods, emerging applications, and future directions in multimodal AI for drug discovery, repurposing, and safety assessment.
Workshop Organizers (Chairs/Co-Chairs):
Bishnu Sarker, PhD (University of North Texas, Denton, TX) (Chair):
email: bishnu.sarker@unt.edu
Heejun Kim, PhD (University of North Texas, Denton, TX):
email: heejun.kim@unt.edu
Sumaiya Shomaji, PhD (University of Kansas, Lawrence, KS):
email: shomaji@ku.edu
Haihua Chen, PhD (University of North Texas, Denton, TX):
email: haihua.chen@unt.edu
Program Committee:
Animesh Acharjee, PhD (University of Birmingham, Birmingham, UK)
Sabeur Aridhi, PhD (University of Lorraine, Nancy, FR)
Sayane Shome, PhD (University of California, San Francisco, CA, USA)
Sagnik Roy Chowdhury, PhD (University of North Texas, TX, USA)
Jamaine Davis, PhD (Belmont University, Nashville, TN, USA)
Sadman Fairuz Shishir (University of Kansas, Lawrence, KS, USA)
Dr. Xiaozhong Liu, Associate Professor, Worcester Polytechnic Institute, MA, USA.
Dr. Ying Ding, Bill & Lewis Suit Professor, University of Texas at Austin, TX, USA.
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