Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning.

ABSTRACT: Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD +

SUBMITTER: Wang B 

PROVIDER: S-EPMC9143325 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature