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KAIDD@CIKM2023
KAIDD@CIKM2023

The 1st Workshop on Knowledge-enhanced Artificial Intelligence in Drug Discovery (KAIDD)

The 1st Workshop on Knowledge-enhanced Artificial Intelligence in Drug Discovery (KAIDD)

Overview

Artificial Intelligence (AI) in drug discovery is a rapidly evolving field that combines computational methods with biological knowledge and applications. Traditionally, the process of developing a new drug has been time-consuming and expensive, spanning several years and costing billions of dollars. The emergence of AI technologies offers the potential to significantly reduce both the timeline and cost involved in this critical endeavour. However, it is crucial to acknowledge that AI applications in pharmacy and drug discovery require a high degree of interpretability and transparency. The integration of domain knowledge into AI models becomes paramount to ensure the reliability and trustworthiness of the generated results. In light of these considerations, we propose a workshop on 'Knowledge-enhanced Artificial Intelligence in Drug Discovery (KAIDD).' This workshop aims to explore the profound impact of incorporating various knowledge databases into the development of explainable AI models for drug discovery. Participants will have the opportunity to delve into cutting-edge research, methodologies, and practical applications that leverage the fusion of AI techniques with domain-specific knowledge. 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.

The KAIDD@CIKM2023 workshop will be held as a half-day event in conjunction with

CIKM 2023⁠

Call for Papers

This workshop focuses on two themes

Integration of domain knowledge in AI models for drug discovery: This theme explores the seamless integration of domain-specific knowledge, such as biological, chemical, and pharmacological information, into AI models, enabling more accurate and reliable predictions and insights for drug discovery.

Explainable AI approaches for pharmaceutical research: This theme focuses on the development and application of transparent and interpretable AI techniques in the pharmaceutical domain, ensuring that the decisions made by AI models in drug discovery can be explained and understood by researchers, regulators, and other stakeholders.

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

Submission Instructions

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.

Submission website:

https://easychair.org/conferences/?conf=kaidd23⁠

Important Dates

Time zone:

Anywhere on Earth (AoE)⁠

⁠

Submission deadline

August 31, 2023

Review period

August 31, 2023 – September 22, 2023

Notification date

September 22, 2023

Final version submission date

September 29, 2023

Organizers

Dr. Qingpeng Zhang (chair)

Musketeers Foundation Institute of Data Science and Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong; Laboratory of Data Discovery for Health (D²4H), Hong Kong, China.

Dr. Jiandong Zhou (local co-chair)

Warwick Medical School (WMS), the University of Warwick, Coventry, England, UK.

Dr. Tingting Zhu (local co-chair)

Department of Engineering Science, University of Oxford, Oxford, UK.

Dr. Qian Chu (co-chair)

Department of Thoracic Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.

Dr. Zhen Li (co-chair)

Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.