Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain. (arXiv:2205.02169v1 [q-bio.BM])

Learning-to-rank, a machine learning technique widely used in information
retrieval, has recently been applied to the problem of ligand-based virtual
screening, to accelerate the early stages of new drug development. Ranking
prediction models learn based on ordinal relationships, making them suitable
for integrating assay data from various environments. Existing studies of rank
prediction in compound screening have generally used a learning-to-rank method
called RankSVM. However, they have not been compared with or validated against
the gradient boosting decision tree (GBDT)-based …

arxiv bio boosting decision gradient learning learning-to-rank tree virtual