Learning to Rank Articles for Molecular Queries
Keywords:Health And Medicine, Molecule Ranking, Molecule Discovery
AbstractThe cost of developing new drugs is estimated at billions of dollars per year. Identification of new molecules for drugs involves scanning existing bio-medical literature for relevant information. As the potential drug molecule is novel, retrieval of relevant information using a simple direct search is less likely to be productive. Identifying relevant papers is therefore a more complex and challenging task, which requires searching for information on molecules with similar characteristics to the novel drug. In this paper, we present the novel task of ranking documents based on novel molecule queries. Given a chemical molecular structure, we wish to rank medical papers that will contribute to a researcher's understanding of the novel molecule drug potential. We present a set of ranking algorithms and molecular embeddings to address the task. An extensive evaluation of the algorithms is performed over the molecular embeddings, studying their performance on a benchmark retrieval corpus, which we share with the community. Additionally, we introduce a heterogeneous edge-labeled graph embedding approach to address the molecule ranking task. Our evaluation shows that the proposed embedding model can significantly improve molecule ranking methods. The system is currently deployed in a targeted drug delivery and personalized medicine research laboratory.
How to Cite
Nordon, G., Magen, A., Guy, I., & Radinsky, K. (2022). Learning to Rank Articles for Molecular Queries. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12594-12600. https://doi.org/10.1609/aaai.v36i11.21532
IAAI Technical Track on Emerging Applications of AI