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Piecewise Hypersphere Modeling by Particle Swarm Optimization in QSAR Studies of Bioactivities of Chemical Compounds

https://doi.org/10.1021/ci049642m
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/(sici)1099-128x(199603)10:2<119::aid-cem409>3.0.co;2-4openalex-snapshot
Evolutionary variable selection in regression and PLS analyses
inferredcites →https://doi.org/10.1002/jcc.20094openalex-snapshot
Hybridized particle swarm algorithm for adaptive structure training of multilayer feed‐forward neural network: QSAR studies of bioactivity of organic compounds
inferredcites →https://doi.org/10.1002/jps.2600640712openalex-snapshot
Formulation of De Novo Substituent Constants in Correlation Analysis: Inhibition of Dihydrofolate Reductase by 2,4‐Diamino‐5‐ (3,4‐dichlorophenyl)‐6‐substituted Pyrimidines
inferredcites →https://doi.org/10.1016/s0169-7439(00)00113-1openalex-snapshot
Variable and subset selection in PLS regression
inferredcites →https://doi.org/10.1021/ci00001a013openalex-snapshot
A comment on nomenclature and the unsaturated bond
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Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics
inferredcites →https://doi.org/10.1021/ci00063a007openalex-snapshot
Review of ring perception algorithms for chemical graphs
inferredcites →https://doi.org/10.1021/ci010291aopenalex-snapshot
Toward an Optimal Procedure for Variable Selection and QSAR Model Building
inferredcites →https://doi.org/10.1021/ci950204copenalex-snapshot
Neural Network Studies. 2. Variable Selection
inferredcites →https://doi.org/10.1021/ci960047xopenalex-snapshot
GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists
inferredcites →https://doi.org/10.1021/ci980033mopenalex-snapshot
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
inferredcites →https://doi.org/10.1021/ci980088oopenalex-snapshot
GA Strategy for Variable Selection in QSAR Studies: Application of GA-Based Region Selection to a 3D-QSAR Study of Acetylcholinesterase Inhibitors
inferredcites →https://doi.org/10.1021/jm0105427openalex-snapshot
Antitumor Agents. 213. Modeling of Epipodophyllotoxin Derivatives Using Variable SelectionkNearest Neighbor QSAR Method
inferredcites →https://doi.org/10.1109/4235.985692openalex-snapshot
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
inferredcites →https://doi.org/10.1109/tevc.2016.2568298openalex-snapshot
IEEE World Congress on Computational Intelligence

What points at this work

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inferred← citeshttps://doi.org/10.1002/chin.200532195openalex-snapshot
Piecewise Hypersphere Modeling by Particle Swarm Optimization in QSAR Studies of Bioactivities of Chemical Compounds.
inferred← citeshttps://doi.org/10.1002/qsar.200630156openalex-snapshot
Comparison of Shuffling‐Adaptive Neuro Fuzzy Inference System (Shuffling‐ANFIS) with Conventional ANFIS as Feature Selection Methods for Nonlinear Systems
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_133openalex-snapshot
Drug Design with Artificial Intelligence Methods
inferred← citeshttps://doi.org/10.1007/978-3-540-36266-1_10openalex-snapshot
Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
inferred← citeshttps://doi.org/10.1007/springerreference_60305openalex-snapshot
Drug Design with Artificial Intelligence Methods
inferred← citeshttps://doi.org/10.1016/j.aca.2005.07.033openalex-snapshot
QSAR analysis of a series of 2-aryl(heteroaryl)-2,5-dihydropyrazolo[4,3-c]quinolin-3-(3H)-ones using piecewise hyper-sphere modeling by particle swarm optimization
inferred← citeshttps://doi.org/10.1016/j.asoc.2007.01.009openalex-snapshot
The landscape adaptive particle swarm optimizer
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.06.016openalex-snapshot
Chemometrics tools in QSAR/QSPR studies: A historical perspective
inferred← citeshttps://doi.org/10.1016/j.chemolab.2016.10.016openalex-snapshot
Interpretable linear and nonlinear quantitative structure-selectivity relationship (QSSR) modeling of a biomimetic catalytic system by particle swarm optimization based sparse regression
inferred← citeshttps://doi.org/10.1016/j.ejps.2006.04.002openalex-snapshot
Boosting support vector regression in QSAR studies of bioactivities of chemical compounds
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2014.11.006openalex-snapshot
Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization
inferred← citeshttps://doi.org/10.1016/j.talanta.2006.04.039openalex-snapshot
Optimized sample-weighted partial least squares
inferred← citeshttps://doi.org/10.1016/j.talanta.2006.05.058openalex-snapshot
Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies
inferred← citeshttps://doi.org/10.1021/ci700047xopenalex-snapshot
Radial Basis Function Network-Based Transform for a Nonlinear Support Vector Machine as Optimized by a Particle Swarm Optimization Algorithm with Application to QSAR Studies
inferred← citeshttps://doi.org/10.1039/c1an15369bopenalex-snapshot
Swarm intelligence metaheuristics for enhanced data analysis and optimization
inferred← citeshttps://doi.org/10.1080/07391102.2015.1084479openalex-snapshot
Structural insights into the interactions of phorbol ester and bryostatin complexed with protein kinase C: a comparative molecular dynamics simulation study
inferred← citeshttps://doi.org/10.5740/jaoacint.sge_goodarziopenalex-snapshot
Feature Selection Methods in QSAR Studies
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