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

Abstract

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 bestperforming 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.

ProTox 3.0: a webserver for the prediction of toxicity of chemicals

“…Computational and machine learning models can potentially provide more efficient and cost-effective ways to identify neurotoxicity associated with clinical drugs. Data on known neurotoxic and non-neurotoxic drugs is crucial for training accurate models and was curated from the literature ( 12 , 13 , 20 ). SMOTEVDM—the data sampling method ( 13 ) was used to construct the model.…”

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths

“…The preprocessed data set described above was used to train regression ML models using the Support Vector Machine (SVM) algorithm, which stands out as a favored algorithm in supervised learning, particularly adept at handling scenarios with small sample sizes and high-dimensional data challenges. 49 Each regression model used the DNA sequence as input and predicted ΔF/F at a given wavelength as output. The 18-nucleotide (nt)-long DNA sequences were represented as one-hot encoded (1 × 72) vectors.…”

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors Across Multiple Spectral Wavelengths

“…Training. The preprocessed dataset described above was used to train regression ML models using the Support Vector Machine (SVM) algorithm, which stands out as a favored algorithm in supervised learning, particularly adept at handling scenarios with small sample sizes and high-dimensional data challenges 47 . Each regression model used the DNA sequence as input and predicted ΔF/F at a given wavelength as output.…”