Identification of SARS-CoV-2 viral entry inhibitors using machine learning and cell-based pseudotyped particle assay

Abstract

In response to the pandemic caused by SARS-CoV-2, we constructed a hybrid support vector machine (SVM) classification model using a set of publicly posted SARS-CoV-2 pseudotyped particle (PP) entry assay repurposing screen data to identify novel potent compounds as a starting point for drug development to treat COVID-19 patients. Two different molecular descriptor systems, atom typing descriptors and 3D fingerprints (FPs), were employed to construct the SVM classification models. Both models achieved reasonable performance, with the area under the curve of receiver operating characteristic (AUC-ROC) of 0.84 and 0.82, respectively. The consensus prediction outperformed the two individual models with significantly improved AUC-ROC of 0.91, where the compounds with inconsistent classifications were excluded. The consensus model was then used to screen the 173,898 compounds in the NCATS annotated and diverse chemical libraries. Of the 255 compounds selected for experimental confirmation, 116 compounds exhibited inhibitory activities in the SARS-CoV-2 PP entry assay with IC₅₀ values ranged between 0.17 µM and 62.2 µM, representing an enrichment factor of 3.2. These 116 active compounds with diverse and novel structures could potentially serve as starting points for chemistry optimization for COVID-19 drug discovery.

Keywords: COVID-19; Consensus prediction; Pseudotyped particles assay; SARS-CoV-2; Support vector machine (SVM).

Graphical abstract

Fig. 1

The receiver operating characteristic (ROC) curves of the atom-type-based (ATP) model (AUC = 0.84), 3D atom-pair FP-based model (AUC = 0.82) and the consensus model (AUC = 0.91).

Fig. 2

The flowchart of data processing and model construction. The consensus model was achieved by excluding the disagreed hits of both SVC models.

Fig. 3

Library composition and physicochemical property distribution. (a) Pie chart of library composition in the number and percentage of compounds and histogram of (b) calculated logP and (c) molecular weight for the three compound libraries, Genesis in gray, Sytravon in orange, and NPACT in blue.

Fig. 4

Concentration-response curves for top ranking promising hits. Solid circles represent data from PP entry assay, blank squares represent data from cytotoxicity counter screen.