Identification of Active Molecules against Thrombocytopenia through Machine Learning

Thrombocytopenia,
which is associated with thrombopoietin (TPO)
deficiency, presents very limited treatment options and can lead to
life-threatening complications. Discovering new therapeutic agents
against thrombo­cytopenia has proven to be a challenging task
using traditional screening approaches. Fortunately, machine learning
(ML) techniques offer a rapid avenue for exploring chemical space,
thereby increasing the likelihood of uncovering new drug candidates.
In this study, we focused on computational modeling for drug-induced
megakaryocyte differentiation and platelet production using ML methods,
aiming to gain insights into the structural characteristics of hemato­poietic
activity. We developed 112 different classifiers by combining eight
ML algorithms with 14 molecule features. The top-performing model
achieved good results on both 5-fold cross-validation (with an accuracy
of 81.6% and MCC value of 0.589) and external validation (with an
accuracy of 83.1% and MCC value of 0.642). Additionally, by leveraging
the Shapley additive explanations method, the best model provided
quantitative assessments of molecular properties and structures that
significantly contributed to the predictions. Furthermore, we employed
an ensemble strategy to integrate predictions from multiple models
and performed in silico predictions for new molecules
with potential activity against thrombo­cytopenia, sourced from
traditional Chinese medicine and the Drug Repurposing Hub. The findings
of this study could offer valuable insights into the structural characteristics
and computational prediction of thrombopoiesis inducers.