Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease

Abstract

In the present research, we present the application of a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against Alzheimer's disease (AD). The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D-QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (https://coconut.naturalproducts.net/download) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to check their influence on the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Additionally, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.