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Do Medicinal Chemists Learn from Activity Cliffs? A Systematic Evaluation of Cliff Progression in Evolving Compound Data Sets
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/3527603743openalex-snapshot
Chemoinformatics in Drug Discovery
inferredcites →https://doi.org/10.1016/j.compbiolchem.2007.08.009openalex-snapshot
Workflow based framework for life science informatics
inferredcites →https://doi.org/10.1021/ci060117sopenalex-snapshot
On Outliers and Activity CliffsWhy QSAR Often Disappoints
inferredcites →https://doi.org/10.1021/ci3001138openalex-snapshot
MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs
inferredcites →https://doi.org/10.1021/ci300288fopenalex-snapshot
Frequency of Occurrence and Potency Range Distribution of Activity Cliffs in Bioactive Compounds
inferredcites →https://doi.org/10.1021/ci900450mopenalex-snapshot
Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets
inferredcites →https://doi.org/10.1021/jm100933wopenalex-snapshot
Activity Landscape Representations for Structure−Activity Relationship Analysis
inferredcites →https://doi.org/10.1021/jm201706bopenalex-snapshot
Exploring Activity Cliffs in Medicinal Chemistry
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
inferredcites →https://doi.org/10.1111/j.1747-0285.2011.01150.xopenalex-snapshot
Comprehensive Analysis of Single‐ and Multi‐Target Activity Cliffs Formed by Currently Available Bioactive Compounds
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9780470921920.edm139openalex-snapshot
QSAR and Molecular Modeling Approaches for Prediction of Drug Metabolism
inferred← citeshttps://doi.org/10.1002/9781118763070.ch3openalex-snapshot
QSAR modeling: prediction of biological activity from chemical structure
inferred← citeshttps://doi.org/10.1002/9783527677016.ch1openalex-snapshot
De NovoDesign: From Models to Molecules
inferred← citeshttps://doi.org/10.1002/minf.201600023openalex-snapshot
Advances in Activity Cliff Research
inferred← citeshttps://doi.org/10.1007/978-3-319-56850-8_8openalex-snapshot
Quantitative Structure-Epigenetic Activity Relationships
inferred← citeshttps://doi.org/10.1016/b978-0-12-382030-3.00002-7openalex-snapshot
Lead Discovery and Lead Modification
inferred← citeshttps://doi.org/10.1016/b978-0-12-822249-2.00002-5openalex-snapshot
Approaches using AI in medicinal chemistry
inferred← citeshttps://doi.org/10.1016/j.bmc.2016.08.004openalex-snapshot
Discovery and synthetic optimization of a novel scaffold for hydrophobic tunnel-targeted autotaxin inhibition
inferred← citeshttps://doi.org/10.1016/j.bmcl.2016.02.088openalex-snapshot
7-Substituted umbelliferone derivatives as androgen receptor antagonists for the potential treatment of prostate and breast cancer
inferred← citeshttps://doi.org/10.1016/j.drudis.2014.02.003openalex-snapshot
Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde?
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01073openalex-snapshot
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00741openalex-snapshot
Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00418openalex-snapshot
Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5b00497openalex-snapshot
Anti-HIV Drug Discovery and Development: Current Innovations and Future Trends
inferred← citeshttps://doi.org/10.1021/acsomega.2c02738openalex-snapshot
Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design
inferred← citeshttps://doi.org/10.1021/jm401120gopenalex-snapshot
Recent Progress in Understanding Activity Cliffs and Their Utility in Medicinal Chemistry
inferred← citeshttps://doi.org/10.1038/s41467-024-55082-4openalex-snapshot
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation
inferred← citeshttps://doi.org/10.1039/9781782623731-00120openalex-snapshot
Chemical Similarity, Shape Matching and QSAR
inferred← citeshttps://doi.org/10.1186/s13321-016-0150-6openalex-snapshot
Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem
inferred← citeshttps://doi.org/10.1186/s13321-021-00525-zopenalex-snapshot
Nonadditivity in public and inhouse data: implications for drug design
inferred← citeshttps://doi.org/10.12688/f1000research.2-199.v1openalex-snapshot
Advancing the activity cliff concept
inferred← citeshttps://doi.org/10.21203/rs.3.rs-127217/v1openalex-snapshot
Nonadditivity in Public and Inhouse Data – Implications for Drug Design
inferred← citeshttps://doi.org/10.4155/fmc.14.94openalex-snapshot
Evolution of the Activity Cliff Concept for Structure–Activity Relationship Analysis and Drug Discovery
inferred← citeshttps://doi.org/10.6084/m9.figshare.14904123openalex-snapshot
Additional file 1 of Nonadditivity in public and inhouse data: implications for drug design
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