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Application of Machine Learning To Improve the Results of High-Throughput Docking Against the HIV-1 Protease
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
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Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges
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Consensus scoring for ligand/protein interactions
inferredcites →https://doi.org/10.1021/bi011781zopenalex-snapshot
Anisotropic Dynamics of the JE-2147−HIV Protease Complex: Drug Resistance and Thermodynamic Binding Mode Examined in a 1.09 Å Structure,
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The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service.
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A Connectivity Code for Use in Describing Chemical Structures.
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How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment
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Conditional Probability: A New Fusion Method for Merging Disparate Virtual Screening Results
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Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment
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3D-Quantitative Structure-Activity Relationships of Human Immunodeficiency Virus Type-1 Proteinase Inhibitors: Comparative Molecular Field Analysis of 2-Heterosubstituted Statine Derivatives-Implications for the Design of Novel Inhibitors
inferredcites →https://doi.org/10.1021/jm030644sopenalex-snapshot
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening
inferredcites →https://doi.org/10.1021/jm990352kopenalex-snapshot
Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins
inferredcites →https://doi.org/10.2174/1568026033392778openalex-snapshot
Second-Site NMR Screening and Linker Design
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Application of Machine Learning to Improve the Results of High‐Throughput Docking Against the HIV‐1 Protease.
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Current Status of Virtual Combinatorial Library Design
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Prediction of HIV‐1 Protease Inhibitors Using Machine Learning Approaches
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Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa
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Support Vector Machine Implementation in the Analysis of HIV Species Based on Amino Acid and Dipeptide Sequences
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Molecular Docking: Metamorphosis in Drug Discovery
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Additional file 1 of Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa
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