With the rapid convergence of molecular modeling and artificial intelligence, research areas such as molecular representation learning, drug-drug interactions (DDI), and drug-target interactions (DTI) have become increasingly significant in precision medicine and intelligent drug discovery. At the same time, real-world behavioral data-ranging from electronic health records to patient-reported outcomes on social media-has emerged as a crucial information source for modeling drug mechanisms and evaluating therapeutic effects. This special session aims to explore how structural-level molecular information can be integrated with macro-level behavioral and societal data to develop interpretable, robust, and real-world applicable AI models. We focus on leveraging advanced techniques such as large language models (LLMs), graph neural networks (GNNs), generative models, and multimodal learning to enable breakthroughs in computational pharmacology, behavioral medicine, and public health informatics.

Primary objectives: (1) Developing multimodal AI models that fuse molecular structures and social behavioral data. (2) Building interpretable frameworks for drug interaction prediction (e.g., knowledge-graph-based reasoning). (3) Exploring how behavioral signals (e.g., self-reports from social platforms) affect drug efficacy and safety assessment.

The special session invites submissions addressing (but not limited to) the following areas:

  • Theoretical Advances:
    • Modeling and forecasting drug propagation and risk through social networks.
    • Leveraging social behavioral data (e.g., prescription records, patient forums) to detect drug side effects.
    • Graph-based learning for molecular structural representation.
    • Integration of molecular data with patient behavioral logs.
  • Technical Innovations:
    • LLM-based molecular understanding and drug-target reasoning.
    • Multi-task learning frameworks for DDI, DTI, and social impact (e.g., drug misuse risk) prediction.
    • Federated learning for privacy-sensitive behavioral health data.
    • Generative modeling for de novo drug design and behavioral impact estimation.
    • Cross-modal transfer learning across drug-behavior datasets.
  • Applications:
    • Drug repurposing with behavioral data (e.g., from EHRs or social media).
    • Drug safety surveillance via social signal mining.
    • Personalized medication strategies based on behavioral profiles.
  • Ethics and Policy:
    • Bias and underrepresentation in drug-behavior AI models.
    • Privacy-preserving methods for behavioral data (e.g., differential privacy, anonymization).
    • Ethical boundaries and safeguards in AI-driven drug recommendation systems.

Important Dates

  • Special Session Papers Submission: 25 July 2025
  • Acceptance Notification: 15 August 2025
  • Camera-Ready Submission: 01 September 2025
  • Conference Date: 16-18 Oct 2025

Paper submission instruction

Paper submission system is available at: https://cmt3.research.microsoft.com/BESC2025.

All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to BESC 2025, originality, significance and clarity.

Please note:

  • All submissions should use IEEE two-column style. Templates are available from here.
  • All papers must be submitted electronically through the paper submission system in PDF format only. BESC 2025 accepts special session papers in 6 pages. The page count excludes the references.
  • Paper review will be double-blind, and submissions not properly anonymized will be desk-rejected without review.
  • Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings.
  • Papers must be clearly submitted in English and will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation.
  • Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work.
  • Accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements and indexed by EI. Top quality papers after presented in the conference will be selected for extension and publication in several special issues of international journals, e.g., IEEE Transactions on Computational Social Systems (IEEE), IEEE Transactions on Learning Technologies (IEEE), CCF Transactions on Pervasive Computing and Interaction (Springer), EURASIP Journal on Image and Video Processing (Springer), World Wide Web Journal (Springer), Social Network Analysis and Mining (Springer), Human-Centric Intelligent Systems (Springer), Natural Language Processing (Elsevier), Health Information Science and Systems (Springer), Web Intelligence (IOS Press), etc.
  • The use of artificial intelligence (AI)–generated text in an article shall be disclosed in the acknowledgements section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.

Organizing Committee

Program Co-Chairs:
  • Dr. Yanhui Gu (Nanjing Normal University, China)
  • Dr. Shujiang Huang (Nanjing University, China)
  • Dr. Baoxing Shen (Nanjing Normal University, China)
Program Committee Members
  • Prof. Hong Lu (Fudan University, China)
  • Prof. Weiguang Qu (Nanjing Normal University, China)
  • Dr. Peirong Ma (Nanjing Normal University, China)
  • Dr. Hongyan Wu (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China)
  • Dr. Xinyu Zhou (Nanyang Technological University, Singapore)
  • Dr. Cong Wang (University of California, San Francisco, United States)
  • Dr. Qianyu Guo (Shanghai Jiao Tong University, China)
  • Dr. ShuYong Gao (Fudan University, China)
  • Dr. Wu Ran (Shanghai Jiao Tong University, China)
  • Dr. Qing Lin (Nanyang Technological University, Singapore)