ArticleJournal of medicinal chemistry2025
Accelerating the Hit-To-Lead Optimization of a SARS-CoV-2 Mpro Inhibitor Series by Combining High-Throughput Medicinal Chemistry and Computational Simulations.
Article in Journal of medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Recent Advances in the Design of Inhibitors Targeting the Viral Entry and Replication of the SARS-CoV-2 Virus, Driven by In Silico Approaches.Molecules (Basel, Switzerland) · 2026Review
- Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026Article
- Chromene-Thiazole Derivatives as Potential SARS-CoV‑2 MACS omega · 2025Article
- Qishiwei Zhenzhu Pills Protect Against Cerebral Ischemia via the P53/Cytochrome C/Apoptotic Protease Activating Factor 1-Mediated Mitochondrial Apoptosis Pathway.CNS neuroscience & therapeutics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we performed the hit-to-lead optimization of a SARS-CoV-2 Mpro diazepane hit (identified by computational methods in a previous work) by combining computational simulations with high-throughput medicinal chemistry (HTMC). Leveraging the 3D structural information of Mpro, we refined the original hit by targeting the S1 and S2 binding pockets of the protein. Additionally, we identified a novel exit vector pointing toward the S1' pocket, which significantly enhanced the binding affinity. This strategy enabled us to transform, rapidly with a limited number of compounds synthesized, a 14 μM hit into a potent 16 nM lead compound, for which key pharmacological properties were subsequently evaluated. This result demonstrated that combining computational technologies such as machine learning, molecular docking, and molecular dynamics simulation with HTMC can efficiently accelerate hit identification and subsequent lead generation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.