After more than two decades of relentless effort and scientific research, siRNA (small interfering RNA) therapy has emerged as a revolutionary molecular intervention technique in the global pharmaceutical industry, demonstrating tremendous potential and broad prospects for development. Despite this, the development of siRNA drugs still faces numerous challenges, including drug delivery, off-target effects, and immunotoxicity, which constrain their efficacy and hinder the broader clinical application. In the field of nucleic acid drug development, the key lies in designing siRNAs that can effectively circumvent these deficiencies. With the advancement of artificial intelligence technology, especially the computer-aided siRNA design algorithms based on deep learning, there has been widespread interest. However, there is a current lack of a unified and high-quality siRNA efficacy database to support the training of algorithms and the development of tools.
    In response to this need, our project has successfully established an experimentally validated siRNA efficacy database, siRNAEfficacyDB, containing 3545 records of siRNA drug efficacy data, covering their sequences and various feature information, and utilizing multiple prediction algorithms to evaluate the efficacy of siRNAs. Moreover, we have developed a user-friendly website, enabling users to easily access, query, and analyze siRNA efficacy data, further supporting research in computer-aided siRNA design and providing a valuable resource platform for the study of small nucleic acid drugs.