ArticleBioinformatics and biology insights2025
Identification of Potent HDAC6 Inhibitors for Breast Cancer Through Multi-Stage In Silico Modeling.
Article in Bioinformatics and biology insights, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histone deacetylases (HDACs) are essential epigenetic regulators, with HDAC6 overexpression linked to estrogen receptor (ER) activity and breast cancer progression. While several HDAC6 inhibitors have been investigated, their clinical success remains limited due to toxicity and off-target effects, necessitating the discovery of novel, selective inhibitors. This study employs a multi-stage computational approach to identify potent HDAC6 inhibitors for breast cancer therapy. A large-scale virtual screening of 264 834 compounds was conducted, followed by molecular docking, molecular dynamics (MD) simulations (100 ns), molecular mechanics/generalized born surface area (MM/GBSA) binding free energy calculations, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. The HDI-3 emerged as the most promising candidate among replicate simulations, exhibiting a substantially favorable MM/GBSA binding free energy of -130.67 kcal/mol-indicative of strong thermodynamic stability and stronger binding affinity compared to reference inhibitors Trichostatin A and Ricolinostat. Molecular dynamics simulations revealed that HDI-3 maintained structural stability, persistent key interactions with active site residues (ASP649, HIS651, ASP742), and low conformational fluctuations. The ADMET evaluation confirmed HDI-3's favorable pharmacokinetic properties, including optimal bioavailability, non-mutagenicity, and low hepatotoxicity. Essential dynamics and principal component analysis further validated its stable binding profile. While these findings highlight HDI-3 as a selective and pharmacologically viable HDAC6 inhibitor, it is important to acknowledge that the results are entirely computational. Therefore, experimental validation is essential to confirm the compound's efficacy and safety. This integrated computational pipeline provides an efficient strategy to accelerate targeted drug discovery, laying the groundwork for future experimental investigations.
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.