ArticleMethods in molecular biology (Clifton, N.J.)2015
Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).
Article in Methods in molecular biology (Clifton, N.J.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Seaweed bioactives: A comprehensive review of their nutritional, medicinal, and functional roles.iScience · 2026Review
- Transparent Machine Learning Model to Understand Drug Permeability through the Blood-Brain Barrier.Journal of chemical information and modeling · 2024Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- GGL-Tox: Geometric Graph Learning for Toxicity Prediction.Journal of chemical information and modeling · 2021Article
- The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.Molecules (Basel, Switzerland) · 2020Article
- Development and Testing of Druglike Screening Libraries.Journal of chemical information and modeling · 2019Article
- Receptor Binding Affinities of Synthetic Cannabinoids Determined by Non-Isotopic Receptor Binding Assay.Toxicological research · 2019Article
- Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.International journal of molecular sciences · 2018Review
- Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.The AAPS journal · 2018Review
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Authors and funding
2 authors.
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Abstract
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. 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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