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Using Information from Historical High-Throughput Screens to Predict Active Compounds
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Enhancing the Effectiveness of Ligand‐Based Virtual Screening Using Data Fusion
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Predicting ligand binding to proteins by affinity fingerprinting
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Novel trends in high-throughput screening
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Biodiversity of small molecules – a new perspective in screening set selection
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Novel miniaturized systems in high-throughput screening
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Rethinking Molecular Similarity: Comparing Compounds on the Basis of Biological Activity
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A Screening Pattern Recognition Method Finds New and Divergent Targets for Drugs and Natural Products
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Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers
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Identifying Compound-Target Associations by Combining Bioactivity Profile Similarity Search and Public Databases Mining
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Shaping a Screening File for Maximal Lead Discovery Efficiency and Effectiveness: Elimination of Molecular Redundancy
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Bioturbo Similarity Searching: Combining Chemical and Biological Similarity To Discover Structurally Diverse Bioactive Molecules
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Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing
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Hit-Directed Nearest-Neighbor Searching
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Scaffold Hopping Using Two-Dimensional Fingerprints: True Potential, Black Magic, or a Hopeless Endeavor? Guidelines for Virtual Screening
inferredcites →https://doi.org/10.1021/jm9602928openalex-snapshot
The Properties of Known Drugs. 1. Molecular Frameworks
inferredcites →https://doi.org/10.1038/nrd3368openalex-snapshot
Impact of high-throughput screening in biomedical research
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Biological spectra analysis: Linking biological activity profiles to molecular structure
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Display and Analysis of Patterns of Differential Activity of Drugs Against Human Tumor Cell Lines: Development of Mean Graph and COMPARE Algorithm
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“Plate Cherry Picking”: A Novel Semi-Sequential Screening Paradigm for Cheaper, Faster, Information-Rich Compound Selection
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Plate-Based Diversity Selection Based on Empirical HTS Data to Enhance the Number of Hits and Their Chemical Diversity
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Illustration of SSMD, z Score, SSMD*, z* Score, and t Statistic for Hit Selection in RNAi High-Throughput Screens
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Connecting Small Molecules with Similar Assay Performance Profiles Leads to New Biological Hypotheses
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Open-source platform to benchmark fingerprints for ligand-based virtual screening
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Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity
inferredcites →https://doi.org/10.4155/fmc.11.4openalex-snapshot
Effectiveness of 2D Fingerprints for Scaffold Hopping
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
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Additional file 1 of Pharmacological affinity fingerprints derived from bioactivity data for the identification of designer drugs
inferred← citeshttps://doi.org/10.6084/m9.figshare.20648121openalex-snapshot
Additional file 1 of Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis
inferred← citeshttps://doi.org/10.6084/m9.figshare.20648124openalex-snapshot
Additional file 2 of Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis
inferred← citeshttps://doi.org/10.6084/m9.figshare.20648127openalex-snapshot
Additional file 3 of Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis
inferred← citeshttps://doi.org/10.6084/m9.figshare.20648133openalex-snapshot
Additional file 4 of Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis
inferred← citeshttps://doi.org/10.6084/m9.figshare.20648136openalex-snapshot
Additional file 5 of Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis
inferred← citeshttps://doi.org/10.70675/a6af77b4z321dz4ab5z8b89zb2a887c44ddcopenalex-snapshot
Evaluation of the use of public toxicological data for chemical hazard prediction through computational methods
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