ArticleMolecular pharmaceutics2012
Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions.
Article in Molecular pharmaceutics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Green Synthetic and Pharmacological Developments in the Hybrid Quinazolinone Moiety: An Updated Review.Current topics in medicinal chemistry · 2025Review
- First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183.Molecular diversity · 2024Article
- Efficient clustering of large molecular libraries.bioRxiv : the preprint server for biology · 2024Article
- A novel interpretable machine learning model approach for the prediction of TiOScientific reports · 2024Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Article
- Biological Activity Predictions of Ligands Based on Hybrid Molecular Fingerprinting and Ensemble Learning.ACS omega · 2023Article
- Quantitative Structure-Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Predicting protein network topology clusters from chemical structure using deep learning.Journal of cheminformatics · 2022Article
- Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network.Molecular informatics · 2022Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Pushing property limits in materials discoveryChemical science · 2020Article
- The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.Molecules (Basel, Switzerland) · 2020Article
- Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets.Journal of chemical information and modeling · 2020Article
- Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.Drug and alcohol dependence · 2020Article
- Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning.Frontiers in pharmacology · 2020Article
- Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.Briefings in bioinformatics · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB(2) activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB(2) binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.
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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.