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POEM: Parameter Optimization Using Ensemble Methods: Application to Target Specific Scoring Functions

https://doi.org/10.1021/ci050036g
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What this work says

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

inferredcites →https://doi.org/10.1006/jmbi.1996.0477openalex-snapshot
A Fast Flexible Docking Method using an Incremental Construction Algorithm
inferredcites →https://doi.org/10.1006/jmbi.1999.3371openalex-snapshot
Knowledge-based scoring function to predict protein-ligand interactions
inferredcites →https://doi.org/10.1007/3-540-49430-8openalex-snapshot
Neural Networks: Tricks of the Trade
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/978-1-4615-0015-5openalex-snapshot
Introduction to Global Optimization
inferredcites →https://doi.org/10.1016/j.cbpa.2004.06.002openalex-snapshot
Target-biased scoring approaches and expert systems in structure-based virtual screening
inferredcites →https://doi.org/10.1016/j.jmb.2004.01.003openalex-snapshot
Protein Flexibility in Ligand Docking and Virtual Screening to Protein Kinases
inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1021/jm0003992openalex-snapshot
Detailed Analysis of Scoring Functions for Virtual Screening
inferredcites →https://doi.org/10.1021/jm980536jopenalex-snapshot
A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
inferredcites →https://doi.org/10.1021/jp973084fopenalex-snapshot
All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
inferredcites →https://doi.org/10.1023/a:1007913026166openalex-snapshot
Multiple automatic base selection: Protein–ligand docking based on incremental construction without manual intervention
inferredcites →https://doi.org/10.1023/b:jcam.0000005766.95985.7eopenalex-snapshot
A novel scoring function for molecular docking
inferredcites →https://doi.org/10.1038/nrd894openalex-snapshot
ATPases as drug targets: learning from their structure
inferredcites →https://doi.org/10.1080/00401706.1992.10484919openalex-snapshot
Design and Analysis of Experiments
inferredcites →https://doi.org/10.1088/0954-898x_8_3_004openalex-snapshot
Optimal ensemble averaging of neural networks
inferredcites →https://doi.org/10.1103/physreve.62.2089openalex-snapshot
Fast nearest-neighbor searching for nonlinear signal processing
inferredcites →https://doi.org/10.1109/34.58871openalex-snapshot
Neural network ensembles
inferredcites →https://doi.org/10.2174/0929867043455684openalex-snapshot
Virtual Screening for Kinase Targets
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.

inferred← citeshttps://doi.org/10.1002/9783527619368.ch16openalex-snapshot
Docking and Scoring for Structure‐based Drug Design
inferred← citeshttps://doi.org/10.1002/cem.1367openalex-snapshot
Multivariate assessment of virtual screening experiments
inferred← citeshttps://doi.org/10.1002/minf.202200034openalex-snapshot
Towards an Enrichment Optimization Algorithm (EOA)‐based Target Specific Docking Functions for Virtual Screening
inferred← citeshttps://doi.org/10.1002/prot.22082openalex-snapshot
Threading without optimizing weighting factors for scoring function
inferred← citeshttps://doi.org/10.1016/b0-08-045044-x/00250-9openalex-snapshot
Structural, Energetic, and Dynamic Aspects of Ligand–Receptor Interactions
inferred← citeshttps://doi.org/10.1016/j.drudis.2009.03.013openalex-snapshot
Targeted scoring functions for virtual screening
inferred← citeshttps://doi.org/10.1016/j.vacuum.2009.01.046openalex-snapshot
Optimization of parameters of the linear TOF-SIMS spectrometer by DOE method
inferred← citeshttps://doi.org/10.1021/ci200377uopenalex-snapshot
Statistical Potential for Modeling and Ranking of Protein–Ligand Interactions
inferred← citeshttps://doi.org/10.1021/ci400079kopenalex-snapshot
Use of Experimental Design To Optimize Docking Performance: The Case of LiGenDock, the Docking Module of Ligen, a New De Novo Design Program
inferred← citeshttps://doi.org/10.1021/ci6005596openalex-snapshot
A Multivariate Approach to Investigate Docking Parameters' Effects on Docking Performance
inferred← citeshttps://doi.org/10.1021/ci700204vopenalex-snapshot
Bootstrap-Based Consensus Scoring Method for Protein–Ligand Docking
inferred← citeshttps://doi.org/10.1021/ci700224eopenalex-snapshot
Distance Dependent Scoring Function for Describing Protein−Ligand Intermolecular Interactions
inferred← citeshttps://doi.org/10.1021/ci700345nopenalex-snapshot
Optimizing the Signal-to-Noise Ratio of Scoring Functions for Protein−Ligand Docking
inferred← citeshttps://doi.org/10.1021/ci7004548openalex-snapshot
AutoShim: Empirically Corrected Scoring Functions for Quantitative Docking with a Crystal Structure and IC50 Training Data
inferred← citeshttps://doi.org/10.1093/bioadv/vbac024openalex-snapshot
Iris Antes 1969–2021
inferred← citeshttps://doi.org/10.1186/1471-2105-11-193openalex-snapshot
A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction
inferred← citeshttps://doi.org/10.1186/s12859-017-1750-5openalex-snapshot
Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
inferred← citeshttps://doi.org/10.2174/157340909787580863openalex-snapshot
Computational Intelligence Methods for Docking Scores
inferred← citeshttps://doi.org/10.3389/fphar.2019.00924openalex-snapshot
Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods
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