Journal Article

Deep Learning-Guided Discovery of Dual Inhibitors of SARS-CoV-2 Entry and 3CL Protease

Shen, Min, Chen, Catherine Z., Zhang, Qi, Zheng, Wei, Xu, Miao, Pavlinov, Ivan, Morales Vasquez, Desarey, Martinez-Sobrido, Luis, Gao, Peng, Ye, Yihong

2026

Overview

The rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for antivirals that are resilient to resistance. Current Food and Drug Administration (FDA)-approved therapies primarily target single viral mechanisms, leaving gaps in efficacy. Here, we developed a Deep Learning-based Activity Screening Model (DLASM), which integrates graph convolutional network with machine learning to identify SARS-CoV-2 inhibitors, using experimental 3-chymotrypsin-like (3CL) main protease assay data. The optimized DLASMs virtually screened ~170,000 compounds from diverse in-house collections and yielded novel hits, several of which not only inhibited the 3CL protease but also blocked viral entry by interfering with heparan sulfate-mediated host interactions. These activities were validated through multiple assays, including 3CL enzymatic inhibition, SARS-CoV-2 pseudotyped particle entry, α-synuclein fibril uptake as a proxy for endocytosis, live virus cytopathic effect, heparan sulfate-dependent entry assay, and a 3D human lung mucociliary tissue model. Molecular docking studies elucidated binding modes at the 3CL protease active site, while molecular dynamics simulations provided insights into compound–heparan sulfate interactions. The identified compounds represent early-stage hits with moderate potency that demonstrate dual-mechanism antiviral activity. Together, these findings establish dual-target inhibition as a promising antiviral strategy, offering not only enhanced potency but also reduced risk of resistance. Moreover, our DLASM framework provides a generalizable pipeline for identifying chemically diverse scaffolds and for broader applications beyond SARS-CoV-2.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: 3CL protease / Antiviral Agents - chemistry / Antiviral Agents - pharmacology / Antiviral drugs / Classification / Coronavirus 3C Proteases - antagonists & inhibitors / Coronavirus 3C Proteases - chemistry / Coronavirus 3C Proteases - metabolism / COVID-19 / COVID-19 Drug Treatment / Deep Learning / deep learning-based activity screening model / Disease transmission / Drug development / Drug Discovery - methods / Drug resistance / dual-target inhibition / Enzymes / graph convolutional networks / Graph Neural Networks / Graph representations / Heparan sulfate / Heparan Sulfate - metabolism / Humans / Machine learning / Molecular Docking Simulation / Molecular Dynamics Simulation / Protease Inhibitors - chemistry / Protease Inhibitors - pharmacology / SARS-CoV-2 / SARS-CoV-2 - drug effects / SARS-CoV-2 - enzymology / SARS-CoV-2 - physiology / Severe acute respiratory syndrome coronavirus 2 / Virus Internalization - drug effects