Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

Neurotoxicity can be resulted from many diverse clinical
drugs,
which has been a cause of concern to human populations across the
world. The detection of drug-induced neurotoxicity (DINeurot) potential
with biological experimental methods always required a lot of budget
and time. In addition, few studies have addressed the structural characteristics
of neurotoxic chemicals. In this study, we focused on the computational
modeling for drug-induced neurotoxicity with machine learning methods
and the insights into the structural characteristics of neurotoxic
chemicals. Based on the clinical drug data with neurotoxicity effects,
we developed 35 different classifiers by combining five different
machine learning methods and seven fingerprint packages. The best-performing
model achieved good results on both 5-fold cross-validation (balanced
accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and
external validation (balanced accuracy of 83.63%, AUC value of 0.87,
and MCC value of 0.67). The model can be freely accessed on the web
server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution
of several key molecular properties between neurotoxic and non-neurotoxic
structures. The results indicated that several physicochemical properties
were significantly different between the neurotoxic and non-neurotoxic
compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors
(nHAcc) and donors (nHDon), the
number of rotatable bonds (nRotB), and the number
of aromatic rings (nAR). In addition, 18 structural
alerts responsible for chemical neurotoxicity were identified. The
structural alerts have been integrated with our web server SApredictor
(http://www.sapredictor.cn). The results of this study could provide useful information for
the understanding of the structural characteristics and computational
prediction for chemical neurotoxicity.