ArticleMolecular diversity2024
Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation.
Article in Molecular diversity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R
Indexed as
Identifiers
38582821What 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.