The efficiency of ligand–receptor interaction information alone as new descriptors in QSAR modeling via random forest artificial neural network

Abstract

A new approach is introduced for the construction of a predictive quantitative structure–activity relationship model in which only ligand–receptor (LR) interaction features are used as relevant descriptors. This approach combines the benefit of the random forest (RF) as a new variable selection method with the intrinsic capability of the artificial neural network (ANN). The interaction information of the ligand–receptor (LR) complex was used as molecular docking descriptors. The most relevant descriptors were selected using the RF technique and used as inputs of ANN. The proposed RF ANN (RF‐LM‐ANN) method was optimized and then evaluated by the prediction of pEC50 for some of the azine derivatives as non‐nucleoside reverse transcriptase inhibitors. RF‐LM‐ANN model under the optimal conditions was evaluated using internal (validation) and external test sets. The determination coefficients of the external test and validation sets were 0.88 and 0.89, respectively. The mean square deviation (MSE) values for the prediction of biological activities in the external test and validation sets were found to be 0.10 and 0.11, respectively. The results obtained demonstrated the good prediction ability and high generalizability of the proposed RF‐LM‐ANN model based on the MMDs alone.

Development and validation of immune-based biomarkers and deep learning models for Alzheimer’s disease

“…Some scholars have applied artificial neural networks to the diagnosis of AD based on the information contained in the digital images of SPECT cerebral blood flow assessment ( Świetlik and Białowąs, 2019 ). There is a precedent for combining two machine learning algorithms to diagnose and predict diseases ( Mozafari et al, 2020 ; Xie et al, 2020 ). Still, it is noteworthy that no research has yet used this combination of the two in the field of AD ( Feng et al, 2021 ).…”

On Hosoya Polynomial and Subsequent Indices of C4C8(R) and C4C8(S) Nanosheets

“…The distance based topological indices are utilized in the improvement of QSAR and QSPR where physiochemical properties of compounds are associated with their molecular graph [9,10]. QSAR models give mathematical relationship between the descriptors and biological activities of ligands [11][12][13]. QSPR models use parameters describing the molecular structure to find an optimum quantitative relationship with the prediction of the properties of compounds [14][15][16].…”