1st International Workshop on Artificial Intelligence

in Drug Discovery and Drug Repositioning (AI-DDDR 2024)

in conjunction with

IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024)

 

AI-driven drug discovery and drug repositioning have emerged as innovative approaches in the field of pharmaceutical research, aiming to accelerate the process of drug development and optimize therapeutic outcomes. Traditional drug discovery methods are time-consuming, expensive, and often result in high failure rates. In contrast, AI-driven approaches leverage machine learning algorithms, big data analytics, and computational modeling to streamline various stages of the drug discovery pipeline, from target identification to clinical trials. Similarly, drug repositioning, also known as drug repurposing, involves finding new therapeutic uses for existing drugs, offering a cost-effective and time-efficient alternative to traditional drug development. Overall, AI-driven approaches in drug discovery and drug repositioning represent promising paradigms for revolutionizing pharmaceutical research and development, offering unprecedented opportunities for accelerating the discovery and development of safe and effective therapeutics.

Topics of interest include, but not limited to:

  • Machine Learning and Predictive Modeling: Researchers are increasingly using machine learning techniques such as deep learning, reinforcement learning, and generative adversarial networks to analyze vast amounts of biological, chemical, and clinical data. These models are employed to predict drug-disease associations, predict drug-target interactions, and identify potential drug candidates with desirable pharmacological properties.
  • Multi-Omics Integration: Integrating data from multiple omics layers, including genomics, transcriptomics, proteomics, and metabolomics, enables an understanding of disease mechanisms and drug responses. AI-driven approaches facilitate the integration of diverse omics datasets to uncover novel therapeutic targets and biomarkers for personalized medicine.
  • Network Pharmacology and Systems Biology: Network-based approaches, such as network pharmacology and systems biology, elucidate the complex interactions between drugs, targets, pathways, and diseases. AI algorithms are utilized to construct and analyze biological networks, identify drug repurposing opportunities, and predict synergistic drug combinations for enhanced therapeutic efficacy.
  • Virtual Screening and Drug Design: Virtual screening techniques, powered by AI algorithms, enable the rapid screening of large chemical libraries to identify potential drug candidates. Additionally, AI-driven drug design platforms facilitate the generation of novel molecular structures with optimized drug-like properties, accelerating the lead optimization process.

 

Paper Submission

Papers should be formatted to IEEE Proceedings Manuscript Formatting Guidelines. A PDF file in 2-column format should be submitted via online BIBM paper submission system. The length of a paper can be 8 pages at most.

 

Publication

All accepted papers will be published in the BIBM proceedings and IEEE Xplore Digital Library. Selected high-quality papers will be invited for publication of their extended versions in Applied Sciences (SCIE-indexed).

 

Important Dates

  • Paper submission due: October 27, 2024 November 3, 2024
  • Acceptance notification: November 8, 2024 November 11, 2024
  • Camera-ready paper due: November 21, 2024 November 23, 2024
  • Workshop date: December 3, 2024

 

Organizers

  Program Chairs

  • Young-Rae Cho, Yonsei University - Mirae Campus, youngcho@yonsei.ac.kr

  Program Committee

  • TBD

 

Program

  • TBD

 

Last Updated 11/11/2024