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Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs
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Polymer design using machine learning: A quest for high glass transition temperature
inferred← citeshttps://doi.org/10.1016/j.synthmet.2025.117986openalex-snapshot
Designing high electron affinity small molecule acceptors through comprehensive chemical library generation
inferred← citeshttps://doi.org/10.1016/j.vascn.2026.108426openalex-snapshot
Machine learning modeling of acute inhalation toxicity using an RFA-RFR framework, supported by explainable AI and SALI-based activity cliff analysis
inferred← citeshttps://doi.org/10.1016/j.yrtph.2018.11.002openalex-snapshot
Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use
inferred← citeshttps://doi.org/10.1016/s0079-6468(10)49004-9openalex-snapshot
Computational Analysis of Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.est.6c06820openalex-snapshot
Activity Landscape Roughness Anticipates Machine-Learning Reliability Across Environmental Chemistry Endpoints
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00947openalex-snapshot
Using Graph Databases to Investigate Trends in Structure–Activity Relationship Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01344openalex-snapshot
Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00841openalex-snapshot
Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00903openalex-snapshot
Roughness of Molecular Property Landscapes and Its Impact on Modellability
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.3c01008openalex-snapshot
Evaluation of the Topology Space of DNA-Encoded Libraries
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00056openalex-snapshot
In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00639openalex-snapshot
Denoising Drug Discovery Data for Improved Absorption, Distribution, Metabolism, Excretion, and Toxicity Property Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00110openalex-snapshot
The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00415openalex-snapshot
Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00506openalex-snapshot
iCliff Taylor’s Version: Robust and Efficient Activity Cliff Determination
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01982openalex-snapshot
Deep Learning vs Classical Methods in Potency and ADME Prediction: Insights from a Computational Blind Challenge
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00290openalex-snapshot
Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00776openalex-snapshot
Activity Landscape Plotter: A Web-Based Application for the Analysis of Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00298openalex-snapshot
Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00114openalex-snapshot
General Approach to Estimate Error Bars for Quantitative Structure–Activity Relationship Predictions of Molecular Activity
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00188openalex-snapshot
Study of Data Set Modelability: Modelability, Rivality, and Weighted Modelability Indexes
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00313openalex-snapshot
Regression Modelability Index: A New Index for Prediction of the Modelability of Data Sets in the Development of QSAR Regression Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00376openalex-snapshot
Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00661openalex-snapshot
Three-Dimensional Activity Landscape Models of Different Design and Their Application to Compound Mapping and Potency Prediction
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.2c02015openalex-snapshot
“Magic Chloro”: Profound Effects of the Chlorine Atom in Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jpcc.5c03868openalex-snapshot
Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization
inferred← citeshttps://doi.org/10.1021/acschembio.6b00029openalex-snapshot
Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data
inferred← citeshttps://doi.org/10.1021/acschembio.6b00706openalex-snapshot
What Can We Learn from Bioactivity Data? Chemoinformatics Tools and Applications in Chemical Biology Research
inferred← citeshttps://doi.org/10.1021/acsinfecdis.0c00853openalex-snapshot
Activity Comparison of Epigenetic Modulators against the Hemoprotozoan Parasites Babesia divergens and Plasmodium falciparum
inferred← citeshttps://doi.org/10.1021/acsinfecdis.4c00936openalex-snapshot
Genetic and Cheminformatic Characterization of Mycobacterium tuberculosis Inhibitors Discovered in the Molecular Libraries Small Molecule Repository
inferred← citeshttps://doi.org/10.1021/acsmedchemlett.1c00251openalex-snapshot
The Growing Importance of Chirality in 3D Chemical Space Exploration and Modern Drug Discovery Approaches for Hit-ID
inferred← citeshttps://doi.org/10.1021/acsmedchemlett.4c00148openalex-snapshot
FOCUS on NOD2: Advancing IBD Drug Discovery with a User-Informed Machine Learning Framework
inferred← citeshttps://doi.org/10.1021/acsomega.0c03659openalex-snapshot
Quantitative Comparison of Three-Dimensional Activity Landscapes of Compound Data Sets Based upon Topological Features
inferred← citeshttps://doi.org/10.1021/acsomega.1c01314openalex-snapshot
Chemical Space Exploration of DprE1 Inhibitors Using Chemoinformatics and Artificial Intelligence
inferred← citeshttps://doi.org/10.1021/acsomega.1c03250openalex-snapshot
Navigating into the Chemical Space of Monoamine Oxidase Inhibitors by Artificial Intelligence and Cheminformatics Approach
inferred← citeshttps://doi.org/10.1021/acsomega.3c03225openalex-snapshot
Machine Learning Approaches to Investigate the Structure–Activity Relationship of Angiotensin-Converting Enzyme Inhibitors
inferred← citeshttps://doi.org/10.1021/acsomega.3c07920openalex-snapshot
Cheminformatics Analysis of the Multitarget Structure–Activity Landscape of Environmental Chemicals Binding to Human Endocrine Receptors
inferred← citeshttps://doi.org/10.1021/acsomega.4c05878openalex-snapshot
Machine Learning-Assisted Designing and Screening of Polymers with a High Melting Point: Database Visualization and Synthetic Feasibility Assessment
inferred← citeshttps://doi.org/10.1021/acsomega.4c06146openalex-snapshot
In Silico Structural Insights and Potential Inhibitor Identification Based on the Benzothiazole Core for Targeting Leishmania major Pteridine Reductase 1
inferred← citeshttps://doi.org/10.1021/acsomega.9b00595openalex-snapshot
Integrating the Structure–Activity Relationship Matrix Method with Molecular Grid Maps and Activity Landscape Models for Medicinal Chemistry Applications
inferred← citeshttps://doi.org/10.1021/acsomega.9b02221openalex-snapshot
Evolving Concept of Activity Cliffs
inferred← citeshttps://doi.org/10.1021/cb3001028openalex-snapshot
Rethinking Molecular Similarity: Comparing Compounds on the Basis of Biological Activity
inferred← citeshttps://doi.org/10.1021/ci100091eopenalex-snapshot
Rationalizing Three-Dimensional Activity Landscapes and the Influence of Molecular Representations on Landscape Topology and the Formation of Activity Cliffs
inferred← citeshttps://doi.org/10.1021/ci100197bopenalex-snapshot
Similarity−Potency Trees: A Method to Search for SAR Information in Compound Data Sets and Derive SAR Rules
inferred← citeshttps://doi.org/10.1021/ci200081kopenalex-snapshot
Consensus Models of Activity Landscapes with Multiple Chemical, Conformer, and Property Representations
inferred← citeshttps://doi.org/10.1021/ci2001692openalex-snapshot
Extraction of Discontinuous Structure–Activity Relationships from Compound Data Sets through Particle Swarm Optimization
inferred← citeshttps://doi.org/10.1021/ci2002473openalex-snapshot
From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis
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