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Virtual Screening System for Finding Structurally Diverse Hits by Active Learning

https://doi.org/10.1021/ci700085q
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Improving Generalization with Active Learning
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Kinomics—structural biology and chemogenomics of kinase inhibitors and targets
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Reoptimization of MDL Keys for Use in Drug Discovery
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Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins
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Active Learning with Support Vector Machines in the Drug Discovery Process
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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Active Learning with Support Vector Machine Applied to Gene Expression Data for Cancer Classification
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Use of Structure−Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection
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The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding
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Chemical Similarity Searching
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GPCRDB: an information system for G protein-coupled receptors
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GLIDA: GPCR-ligand database for chemical genomic drug discovery
inferredcites →https://doi.org/10.1145/130385.130417openalex-snapshot
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ChemInform Abstract: Virtual Screening System for Finding Structurally Diverse Hits by Active Learning.
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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
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Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction
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Feasibility of Active Machine Learning for Multiclass Compound Classification
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PegaPlus─Interactive Machine Learning by Human Observation for Efficient Clustering and Analysis of Structure–Activity Data
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Improving the Hit Rates of Virtual Screening by Active Learning from Bioactivity Feedback
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Active Learning for Drug Design: A Case Study on the Plasma Exposure of Orally Administered Drugs
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Defining Levels of Automated Chemical Design
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Machine Learning-Assisted Iterative Screening for Efficient Detection of Drug Discovery Starting Points
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Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
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Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization
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An active role for machine learning in drug development
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Small-molecule RAS/RAF inhibitors target RAS-driven cancers via allosteric RAF disruption
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Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines
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Traversing chemical space with active deep learning for low-data drug discovery
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Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors
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Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
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Improving molecular machine learning through adaptive subsampling with active learning
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Active learning of molecular data for task-specific objectives
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Practical constraints with machine learning in drug discovery
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Reducing false positive rate of docking-based virtual screening by active learning
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A guide to machine learning for bacterial host attribution using genome sequence data
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Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing
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Traversing Chemical Space with Active Deep Learning
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Machine learning approaches for drug virtual screening
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