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. 2022 Apr 26;14(5):943.
doi: 10.3390/pharmaceutics14050943.

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

Binyou Wang  1 , Xiaoqiu Tan  2   3   4 , Jianmin Guo  2 , Ting Xiao  2 , Yan Jiao  5 , Junlin Zhao  1 , Jianming Wu  6   7 , Yiwei Wang  2   3   4
Affiliations

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

Binyou Wang et al. Pharmaceutics. .

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 + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy.

Keywords: drug-induced immune thrombocytopenia; k-nearest neighbor; machine learning; structural alert.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Chemical space distribution of compounds in the training and external validation sets.
Figure 2
Figure 2
Distributions of six molecular properties of DITP toxicants and DITP non-toxicants.
Figure 3
Figure 3
(a) Heat map of the Tanimoto similarity index on the training set. (b) Heat map of the Tanimoto similarity index on the external validation set.
Figure 4
Figure 4
(a) The structure of the DITP toxicants with the β-lactam ring correctly identified by the CCMD + KPFP-XGBoost and RDMD + PubChem-k-NN models. (b) Structure of DITP toxicants misclassified by the CCMD + KPFP-XGBoost model.
Figure 5
Figure 5
Structure of two false positives and two DITP toxicants.
Figure 6
Figure 6
The information gain value distributions of the KRFP fragments.
See this image and copyright information in PMC

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