Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets

Cited by (433)

Items 201–300 of 433

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3. Global Analysis Reveals Families of Chemical Motifs Enriched for hERG Inhibitors
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4. Fuzzy Matched Pairs: A Means To Determine the Pharmacophore Impact on Molecular Interaction
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5. Recent developments in SAR visualization
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6. Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design
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8. Chemical space and molecular descriptors for QSAR studies
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9. Drugs as habitable planets in the space of dark chemical matter
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10. DeepAS – Chemical language model for the extension of active analogue series
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11. VAMMPIRE-LORD: A Web Server for Straightforward Lead Optimization Using Matched Molecular Pairs
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12. Extraction of SAR information from activity cliff clusters via matching molecular series
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13. Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications
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14. The SAR Matrix Method and an Artificially Intelligent Variant for the Identification and Structural Organization of Analog Series, SAR Analysis, and Compound Design
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15. Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs
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16. Systematic mining of analog series with related core structures in multi-target activity space
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17. Systematic mapping of R-group space enables the generation of an R-group replacement system for medicinal chemistry
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18. Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study
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19. Follow-up: Prospective compound design using the ‘SAR Matrix’ method and matrix-derived conditional probabilities of activity
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20. Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account
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21. Automated molecule editing in molecular design
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22. SAR Transfer across Different Targets
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23. AnalogExplorer: A New Method for Graphical Analysis of Analog Series and Associated Structure–Activity Relationship Information
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24. Matched Molecular Series: Measuring SAR Similarity
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25. Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs
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26. Exploring Structure–Activity Relationships with Three‐Dimensional Matched Molecular Pairs—A Review
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27. Quantum Mechanical Investigation of Three-Dimensional Activity Cliffs Using the Molecules-in-Molecules Fragmentation-Based Method
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28. Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications
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29. Systematic assessment of coordinated activity cliffs formed by kinase inhibitors and detailed characterization of activity cliff clusters and associated SAR information
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30. In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints
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31. Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel
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32. Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks
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33. Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome
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34. A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
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35. Mechanism-based bipartite matching molecular series graphs to identify structural modifications of receptor ligands that lead to mechanism hopping
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36. Activity profile relationships between structurally similar promiscuous compounds
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37. Computational method for estimating progression saturation of analog series
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38. Bioactivity-explorer: a web application for interactive visualization and exploration of bioactivity data
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39. Ligand‐based Activity Cliff Prediction Models with Applicability Domain
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40. Maximum common property: a new approach for molecular similarity
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41. Bayesian global-local shrinkage methods for regularisation in the high dimension linear model
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42. Classification of matching molecular series on the basis of SAR phenotypes and structural relationships
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43. Compound Pathway Model To Capture SAR Progression: Comparison of Activity Cliff-Dependent and -Independent Pathways
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44. Systematic Identification of Matching Molecular Series and Mapping of Screening Hits
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45. OOMMPPAA: A Tool To Aid Directed Synthesis by the Combined Analysis of Activity and Structural Data
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46. Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem
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47. Isomeric Activity Cliffs—A Case Study for Fluorine Substitution of Aminergic G Protein-Coupled Receptor Ligands
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48. Extension of three-dimensional activity cliff information through systematic mapping of active analogs
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49. The Power of MMPA and a Teaching Lesson in Medicinal Chemistry
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50. Systematic Identification of Target Set-Dependent Activity Cliffs
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51. Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors
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52. Specific chemical changes leading to consistent potency increases in structurally diverse active compounds
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53. Global Assessment of Substituents on the Basis of Analogue Series
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54. Using Graph Databases to Investigate Trends in Structure–Activity Relationship Networks
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55. Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity
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56. Computational method for the systematic alignment of analogue series with structure-activity relationship transfer potential across different targets
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57. WONKA: objective novel complex analysis for ensembles of protein–ligand structures
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58. Structural and Activity Profile Relationships Between Drug Scaffolds
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59. Matched molecular pair-based data sets for computer-aided medicinal chemistry
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60. Design of a tripartite network for the prediction of drug targets
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61. Follow-up: Prospective compound design using the ‘SAR Matrix’ method and matrix-derived conditional probabilities of activity
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62. Unveiling high-performance hosts for blue OLEDs via deep learning and high-throughput virtual screening
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63. From obstacle to design advantage: activity cliff aware modeling for small-molecule drug discovery
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64. Uncovering and tackling fundamental limitations of compound potency predictions using machine learning models
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65. Identification of lysosomotropism using explainable machine learning and morphological profiling cell painting data
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66. From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets
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67. Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions
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68. Computational method for the identification of third generation activity cliffs
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69. Computational Method for Structure-Based Analysis of SAR Transfer
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70. Molecule Ideation Using Matched Molecular Pairs
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71. Design of MMP-1 inhibitors via SAR transfer and experimental validation
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72. Bioisosteric Design Identifies Inhibitors of Mycobacterium tuberculosis DNA Gyrase ATPase Activity
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74. Method for Systematic Analogue Search Using the Mega SAR Matrix Database
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75. Integrating Computational Lead Optimization Diagnostics With Analog Design and Candidate Selection
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78. Increasing the Public Activity Cliff Knowledge Base With New Categories of Activity Cliffs
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79. Prediction of Promiscuity Cliffs Using Machine Learning
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81. Kinetic modelling of the P-glycoprotein mediated efflux with a large-scale matched molecular pair analysis
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84. <Review> A Mini-review on Chemoinformatics Approaches for Drug Discovery
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85. Systematic Assessment of Molecular Selectivity at the Level of Targets, Bioactive Compounds, and Structural Analogues
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