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Training Based on Ligand Efficiency Improves Prediction of Bioactivities of Ligands and Drug Target Proteins in a Machine Learning Approach
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.aca.2012.09.021openalex-snapshot
Large-scale prediction of drug–target interactions using protein sequences and drug topological structures
inferredcites →https://doi.org/10.1016/j.compbiolchem.2011.10.003openalex-snapshot
Kernel-based data fusion improves the drug–protein interaction prediction
inferredcites →https://doi.org/10.1016/j.drudis.2010.08.004openalex-snapshot
Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation
inferredcites →https://doi.org/10.1016/s1359-6446(04)03069-7openalex-snapshot
Ligand efficiency: a useful metric for lead selection
inferredcites →https://doi.org/10.1016/s1359-6446(05)03386-6openalex-snapshot
Ligand efficiency indices as guideposts for drug discovery
inferredcites →https://doi.org/10.1021/ci050006dopenalex-snapshot
Virtual Screen for Ligands of Orphan G Protein-Coupled Receptors
inferredcites →https://doi.org/10.1021/ci200264hopenalex-snapshot
Computational Screening for Active Compounds Targeting Protein Sequences: Methodology and Experimental Validation
inferredcites →https://doi.org/10.1021/ci800447gopenalex-snapshot
Development and Validation of a Novel Protein−Ligand Fingerprint To Mine Chemogenomic Space: Application to G Protein-Coupled Receptors and Their Ligands
inferredcites →https://doi.org/10.1021/ci900004aopenalex-snapshot
Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors
inferredcites →https://doi.org/10.1021/ci9002624openalex-snapshot
Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects
inferredcites →https://doi.org/10.1038/msb.2011.5openalex-snapshot
Analysis of multiple compound–protein interactions reveals novel bioactive molecules
inferredcites →https://doi.org/10.1038/nbt1068openalex-snapshot
A small molecule–kinase interaction map for clinical kinase inhibitors
inferredcites →https://doi.org/10.1093/bioinformatics/btm580openalex-snapshot
Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor
inferredcites →https://doi.org/10.1093/bioinformatics/btn409openalex-snapshot
Protein-ligand interaction prediction: an improved chemogenomics approach
inferredcites →https://doi.org/10.1093/bioinformatics/btp650openalex-snapshot
Kinase selectivity potential for inhibitors targeting the ATP binding site: a network analysis
inferredcites →https://doi.org/10.1093/nar/30.1.276openalex-snapshot
The Pfam Protein Families Database
inferredcites →https://doi.org/10.1093/nar/gkl999openalex-snapshot
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
inferredcites →https://doi.org/10.1093/nar/gkm948openalex-snapshot
GLIDA: GPCR ligand database for chemical genomics drug discovery database and tools update
inferredcites →https://doi.org/10.1093/nar/gkq1009openalex-snapshot
GPCRDB: information system for G protein-coupled receptors
inferredcites →https://doi.org/10.1093/nar/gkr1065openalex-snapshot
The Pfam protein families database
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
inferredcites →https://doi.org/10.1093/nar/gkr988openalex-snapshot
KEGG for integration and interpretation of large-scale molecular data sets
inferredcites →https://doi.org/10.1093/nar/gks1200openalex-snapshot
The RCSB Protein Data Bank: new resources for research and education
inferredcites →https://doi.org/10.1093/protein/8.2.127openalex-snapshot
LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions
inferredcites →https://doi.org/10.1186/1471-2105-9-363openalex-snapshot
Virtual screening of GPCRs: An in silico chemogenomics approach
inferredcites →https://doi.org/10.1186/1758-2946-3-22openalex-snapshot
Predicting a small molecule-kinase interaction map: A machine learning approach
inferredcites →https://doi.org/10.1371/journal.pcbi.1002195openalex-snapshot
Accelerated Profile HMM Searches
inferredcites →https://doi.org/10.1371/journal.pone.0037608openalex-snapshot
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.cplett.2022.139650openalex-snapshot
Consensus scoring model: A novel approach to the study of EGFR kinase inhibitors
inferred← citeshttps://doi.org/10.1016/j.jmgm.2017.11.003openalex-snapshot
Consensus scoring model for the molecular docking study of mTOR kinase inhibitor
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00136openalex-snapshot
Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00431openalex-snapshot
Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00116openalex-snapshot
Between Descriptors and Properties: Understanding the Ligand Efficiency Trends for G Protein-Coupled Receptor and Kinase Structure–Activity Data Sets
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00316openalex-snapshot
Predicted Biological Activity of Purchasable Chemical Space
inferred← citeshttps://doi.org/10.1021/ci5003262openalex-snapshot
Ligand Efficiency-Based Support Vector Regression Models for Predicting Bioactivities of Ligands to Drug Target Proteins
inferred← citeshttps://doi.org/10.1038/s41429-020-0287-4openalex-snapshot
Antifungal peptides produced by actinomycetes and their biological activities against plant diseases
inferred← citeshttps://doi.org/10.1038/s41467-025-57215-9openalex-snapshot
CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters
inferred← citeshttps://doi.org/10.1038/srep17209openalex-snapshot
Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target
inferred← citeshttps://doi.org/10.1080/17460441.2017.1365056openalex-snapshot
Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective
inferred← citeshttps://doi.org/10.1101/2024.03.10.584340openalex-snapshot
CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters k cat , K m and K i
inferred← citeshttps://doi.org/10.1111/cbdd.12619openalex-snapshot
Ligand Efficiency Outperforms pIC 50 on Both 2D MLR and 3D Co MFA Models: A Case Study on AR Antagonists
inferred← citeshttps://doi.org/10.1111/jcmm.12930openalex-snapshot
Combination therapeutics in complex diseases
inferred← citeshttps://doi.org/10.1186/s13321-015-0110-6openalex-snapshot
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter
inferred← citeshttps://doi.org/10.1208/s12248-017-0092-6openalex-snapshot
Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review
inferred← citeshttps://doi.org/10.3390/molecules23123174openalex-snapshot
An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors
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