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Molecular Fingerprint-Based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions

https://doi.org/10.1021/mp300237z
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Beyond the Scope of Free-Wilson Analysis. 2: Can Distance Encoded R-Group Fingerprints Provide Interpretable Nonlinear Models?
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Trisubstituted Sulfonamides: A New Chemotype for Development of Potent and Selective CB2Receptor Inverse Agonists
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A novel interpretable machine learning model approach for the prediction of TiO2 photocatalytic degradation of air contaminants
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Are 2D fingerprints still valuable for drug discovery?
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General and efficient synthesis of 1,2-dihydropyrrolo[3,4- b ]indol-3-ones via a formal [3 + 2] cycloaddition initiated by C–H activation
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Pushing property limits in materials discovery via boundless objective-free exploration
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A general QSPR protocol for the prediction of atomic/inter-atomic properties: a fragment based graph convolutional neural network (F-GCN)
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Accelerating battery innovation: AI-powered molecular discovery
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BitBIRCH: efficient clustering of large molecular libraries
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Artificial Intelligence: An Introduction to Technical Advancements and Current Biological Applications in Plants
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QSAR modelling using combined simple competitive learning networks and RBF neural networks
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In silico prediction of mosquito repellents for clothing application
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Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models
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Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?
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An overview of neural networks for drug discovery and the inputs used
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How do we further enhance 2D fingerprint similarity searching for novel drug discovery?
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3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction
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Using molecular fingerprints and unsupervised learning algorithms to find simulants of chemical warfare agents
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Different molecular enumeration influences in deep learning: an example using aqueous solubility
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Open-source chemogenomic data-driven algorithms for predicting drug–target interactions
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Predicting Drug Interactions to Unassociated Biomedical Implants Using Machine Learning Techniques and Model Polymers
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Efficient clustering of large molecular libraries
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Benchmarking Small-Dataset Structure-Activity-Relationship Models for Prediction of Wnt Signaling Inhibition
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Implementation of the Grey Wolf Algorithm in Optimization of Artificial Neural Network Method for Fingerprint-Based Toxicity Prediction
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Ligand-Based Virtual Screening with Co-regularised Support Vector Regression
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Clinical Trial Toxicity Prediction by Using Artificial Neural Network Optimized by Grey Wolf Optimization
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E 2 CB2former: Effecitve and Explainable Transformer for CB2 Receptor Ligand Activity Prediction
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Development of Machine Learning and Chemical Language Model‐Based QSAR Models for Predicting Drug Residue Depletion Half‐Lives in Plasma and Tissues of Cattle Across Various Administration Routes
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CheS-Mapper 2.0 for visual validation of (Q)SAR models
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A new chemoinformatics approach with improved strategies for effective predictions of potential drugs
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Predicting protein network topology clusters from chemical structure using deep learning
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Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era
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Mycobacterial Dihydrofolate Reductase Inhibitors Identified Using Chemogenomic Methods and In Vitro Validation
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Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach
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Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes
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Green Synthetic and Pharmacological Developments in the Hybrid Quinazolinone Moiety: An Updated Review
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Application of Artificial Intelligence in Drug Discovery
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Artificial Neural Network in Drug Delivery and Pharmaceutical Research
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ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY
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COMPUTATIONAL QSAR-BASED MACHINE LEARNING APPROACH FOR PREDICTING ACTIVITY OF SGLT2 INHIBITORS USING THE KNIME PLATFORM
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General QSPR Protocol for Atomic/Inter-atomic Properties Predictions: Fragments based Graph Convolutional Neural Network (F-GCN)
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Artificial Intelligence Can Bring Tens of Trillions of Rubles to the Pharmaceutical Industry
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Perfluorooctane sulfonate and bisphenol a strongly interact with bacterial antigens and adversely modulate host immunity
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Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands
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Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning
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Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
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The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis
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New Polymers In Silico Generation and Properties Prediction
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Quantitative Structure–Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective
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Applications of Artificial Neural Network Modeling in Drug Discovery
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Gated Graph Recursive Neural Networks for Molecular Property Prediction
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Molecular modelling and machine learning for the investigation of 2-oxazolidinone ribosomal antibacterials
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Additional file 1 of Predicting protein network topology clusters from chemical structure using deep learning
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.