What does this research mean for the field?

Three novel quinazolin-4-one derivatives (A-100, A-117, and D-119) are identified as potent inhibitors of the SARS-CoV-2 main protease (3CLpro) with favorable binding affinities and drug-like profiles. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to identify novel inhibitors for the SARS-CoV-2 main protease (3CLpro) using computational methods.

March 8, 2026Open Access

In silico discovery of novel quinazolin-4-one derivatives as potent 3CLpro inhibitors using 2D-QSAR, molecular docking, and molecular dynamics

IHIsmail Hdoufane NLNouhaila Ait LahcenCadi Ayyad University WLWissal LimanUniversité Mohammed VI Polytechnique

Key Points

The aim is to identify novel inhibitors for the SARS-CoV-2 main protease (3CLpro) using computational methods.
Designed quinazolin-4-one derivatives using fragment-based drug design (FBDD).
Employed 2D-QSAR modeling for activity prediction and validation.
Conducted molecular docking to assess binding to the 3CLpro active site.
Performed ADMET profiling for pharmacokinetic evaluation of selected compounds.
Utilized molecular dynamics simulations for stability analysis of ligand-protein complexes.
Identified three potent 3CLpro inhibitors: A-100, A-117, D-119.
Showed high predicted activities: 7.459, 7.703, and 7.137 respectively.
Measured strong binding affinities of -8.5, -8.3, and -7.8 kcal/mol.
Demonstrated that D-119 exhibited the lowest RMSD of 0.192 ± 0.032 nm during dynamics, indicating high stability.

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (3CLpro) is a pivotal enzyme in viral replication. 3CLpro serves as a highly conserved and mutation-resistant target, rendering it a promising focus for the development of effective antiviral agents. To develop and assess possible 3CLpro inhibitors, we used a multilayered in silico approach. Quinazolin-4-one derivatives were designed using a fragment-based drug design (FBDD) technique. The promising candidates were chosen based on their predicted activities using a genetic algorithm-multiple linear regression quantitative structure-activity relationship (2D-QSAR) model. The 2D-QSAR model used satisfied both internal and external validation criteria (R 2 = 0.8659, Q 2 loo = 0.8054, and R 2 ext = 0.7385). Following molecular docking to the 3CLpro active site, ADMET (absorption, distribution, metabolism, excretion, toxicity) profiling was performed on the top-ranked compounds to evaluate their pharmacokinetic and toxicity characteristics. The most favorable candidates, A-100, A-117, and D-119, exhibited improved predicted activities (7.459, 7.703, and 7.137, respectively), strong binding affinities of (-8.5, -8.3, and -7.8 kcal mol -1 , respectively), and drug-like profiles. Molecular dynamics (MD) simulations over 150 ns further confirmed the stability and favorable interaction patterns of these ligands within the binding site. MD analysis showed that 3CLpro-D-119 complex exhibited the lowest RMSD (0.192 ± 0.032 nm), indicating the highest structural stability compared with the apo, Baicalein, and the Lead compound. Overall, this integrative computational workflow identified three promising 3CLpro inhibitors with high therapeutic potential, offering an improved foundation for future in vitro and in vivo validation and drug development against SARS-CoV-2.

AIに質問

Bookmark

View Full Paper