Biological Activity Predictions of Ligands Based on Hybrid Molecular Fingerprinting and Ensemble Learning

The biological activity predictions of ligands are an important research direction, which can improve the efficiency and success probability of drug screening. However, the traditional prediction method has the disadvantages of complex modeling and low screening efficiency. Machine learning is considered an important research direction to solve these traditional method problems in the near future. This paper proposes a machine learning model with high predictive accuracy and stable prediction ability, namely, the back propagation neural network cross-support vector regression model (BPCSVR). By comparing multiple molecular descriptors, MACCS fingerprint and ECFP6 fingerprint were selected as inputs, and the stable prediction ability of the model was improved by integrating multiple models and correcting similar samples. We used leave-one-out cross-validation on 3038 samples from six data sets. The coefficient of determination, root mean square error, and absolute error were used as the evaluation parameters. After comparing the multiclass models, the results show that the BPCSVR model has stable prediction ability in different data sets, and the prediction accuracy is higher than other comparison models.

Automated summary

This paper proposes a machine learning model, BPCSVR, which has high predictive accuracy and stable prediction ability. By comparing multiple molecular descriptors, MACCS fingerprint and ECFP6 fingerprint were selected as inputs. The model's stable prediction ability was improved by integrating multiple models and correcting similar samples. After comparing the multiclass models, the results show that the BPCSVR model has stable prediction ability in different data sets and higher prediction accuracy than other comparison models.