Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. 

All submission (.pdf format) must be written in English and use the latest template of ACM CIKM 2023 available at http://www.acm.org/publications/proceedings-template. The concepts and keywords are required. Submissions should be in 2-column sigconf format and cannot exceed 4 pages plus unlimited references.

We also follow CIKM 2023 LLM Policy.Papers that include text generated from a large-scale language model (LLM) such as ChatGPT are prohibited unless this produced text is presented as a part of the paper’s experimental analysis. AI tools may be used to edit and polish authors’ work, such as using LLMs for light editing of their own text (e.g., automate grammar checks, word autocorrect, and other editing work), but text “produced entirely” by AI is not allowed.

All submissions will be double-blind peer reviewed by the program committee and judged by their relevance to the workshop, scientific novelty, and technical quality. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper either remote or on location (strongly preferred). We encourage but do not require authors to release any code and/or datasets associated with their paper.

KAIDD workshop papers will not be included in the ACM proceedings. Authors of accepted papers will have the opportunity to submit extended versions of their work for a full-paper review process and potential publication in Philosophical Transactions of the Royal Society B.

List of Topics

We invite submissions related to KAIDD, including (but not limited to):

• Semantic technologies and ontologies in drug design and optimization
• Knowledge graphs and knowledge-based reasoning in pharmaceutical research
• Interpretable machine learning methods for predicting drug properties
• Data integration and aggregation techniques for large-scale drug discovery datasets
• Mining biomedical literature and scientific databases for drug discovery insights
• Integration of genomic and proteomic data in AI-enabled drug development

Contact

All questions about submissions should be emailed to jiannan.yang@my.cityu.edu.hk.