Gradient Boosted Tree model: A fast track tool for predicting the Atmospheric Pressure Chemical Ionization-Mass Spectrometry signal of antipsychotics based on molecular features and experimental settings

Krmar, Jovana; Džigal, Merima; Stojković, Jovana; Protić, Ana; Otašević, Biljana

doi:10.1016/j.chemolab.2022.104554

Cited by 2 publications

(2 citation statements)

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“…The use of kernel function avoids the dimension disaster, but the selection of kernel function has a great impact on the performance of SVM. Gradient boosting [24,25] trains the new-joined weak classifier based on the negative gradient information of the loss function from the current molecular property prediction model. In each iteration, a weak classifier will be obtained.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

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Double-head transformer neural network for molecular property prediction

et al. 2023

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Existing molecular property prediction methods based on deep learning ignore the generalization ability of the nonlinear representation of molecular features and the reasonable assignment of weights of molecular features, making it difficult to further improve the accuracy of molecular property prediction. To solve the above problems, an end-to-end double-head transformer neural network (DHTNN) is proposed in this paper for high-precision molecular property prediction. For the data distribution characteristics of the molecular dataset, DHTNN specially designs a new activation function, beaf, which can greatly improve the generalization ability of the nonlinear representation of molecular features. A residual network is introduced in the molecular encoding part to solve the gradient explosion problem and ensure that the model can converge quickly. The transformer based on double-head attention is used to extract molecular intrinsic detail features, and the weights are reasonably assigned for predicting molecular properties with high accuracy. Our model, which was tested on the MoleculeNet [1] benchmark dataset, showed significant performance improvements over other state-of-the-art methods.

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Section: Machine Learning Methodsmentioning

confidence: 99%

“…4(c), to adjust the data distribution before output. The calculation formula is shown in Equation (25).…”

Section: Adjusting the Data Distribution Before Outputmentioning

confidence: 99%