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Evolving Interpretable Structure−Activity Relationship Models. 2. Using Multiobjective Optimization To Derive Multiple Models
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.bmc.2004.02.003openalex-snapshot
A model for identifying HERG K+ channel blockers
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Methods for mining HTS data
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Comparison of Ranking Methods for Virtual Screening in Lead-Discovery Programs
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Similarity Searching Using Reduced Graphs
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Lead Hopping Using SVM and 3D Pharmacophore Fingerprints
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The Reduced Graph Descriptor in Virtual Screening and Data-Driven Clustering of High-Throughput Screening Data
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Development and Evaluation of an in Silico Model for hERG Binding
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Scaffold Hopping Using Clique Detection Applied to Reduced Graphs
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Contemporary QSAR Classifiers Compared
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Evolving Interpretable Structure−Activity Relationships. 1. Reduced Graph Queries
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Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets
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Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
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hERG potassium channels and cardiac arrhythmia
inferredcites →https://doi.org/10.1039/b409865jopenalex-snapshot
Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures
inferredcites →https://doi.org/10.1109/3468.650319openalex-snapshot
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation
inferredcites →https://doi.org/10.1145/1143844.1143874openalex-snapshot
The relationship between Precision-Recall and ROC curves
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Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up
inferredcites →https://doi.org/10.5860/choice.27-0936openalex-snapshot
Genetic algorithms in search, optimization, and machine learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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USING CHEMOINFORMATICS TOOLS TO ANALYZE CHEMICAL ARRAYS IN LEAD OPTIMIZATION
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Computational Techniques to Support Hit Triage
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ChemInform Abstract: Evolving Interpretable Structure—Activity Relationship Models. Part 2. Using Multiobjective Optimization to Derive Multiple Models.
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Chemoinformatics at the University of Sheffield 2002–2014
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Computational methods for scaffold hopping
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Reduced Graphs and Their Applications in Chemoinformatics
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QSAR/QSPR Modeling in the Design of Drug Candidates with Balanced Pharmacodynamic and Pharmacokinetic Properties
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QSAR workbench: automating QSAR modeling to drive compound design
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Consensus QSAR model for identifying novel H5N1 inhibitors
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Evolving molecules using multi-objective optimization: applying to ADME/Tox
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Data structures and computational tools for the extraction of SAR information from large compound sets
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Computational Analysis of Structure–Activity Relationships
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De Novo Molecule Design by Translating from Reduced Graphs to SMILES
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The Pharmacophore Network: A Computational Method for Exploring Structure–Activity Relationships from a Large Chemical Data Set
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inSARa: Intuitive and Interactive SAR Interpretation by Reduced Graphs and Hierarchical MCS-Based Network Navigation
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Evolving Interpretable Structure−Activity Relationships. 1. Reduced Graph Queries
inferred← citeshttps://doi.org/10.1021/ci900078hopenalex-snapshot
Use of Reduced Graphs To Encode Bioisosterism for Similarity-Based Virtual Screening
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Improving Quantitative Structure−Activity Relationships through Multiobjective Optimization
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Novel Approach for Chemotype Hopping Based on Annotated Databases of Chemically Feasible Fragments and a Prospective Case Study: New Melanin Concentrating Hormone Antagonists
inferred← citeshttps://doi.org/10.1080/17460441.2018.1496079openalex-snapshot
Challenges with multi-objective QSAR in drug discovery
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Multi-objective optimization methods in novel drug design
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Graph Applications in Chemoinformatics and Structural Bioinformatics
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Graph Applications in Chemoinformatics and Structural Bioinformatics
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