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Advances in the prediction of mouse liver microsomal studies: From machine learning to deep learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/0471141755.ph0708s23openalex-snapshot
In Vitro Drug Metabolism Using Liver Microsomes
inferredcites →https://doi.org/10.1002/cpph.9openalex-snapshot
In Vitro Drug Metabolism Using Liver Microsomes
inferredcites →https://doi.org/10.1002/jcc.21707openalex-snapshot
PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
inferredcites →https://doi.org/10.1002/jcc.24764openalex-snapshot
Deep learning for computational chemistry
inferredcites →https://doi.org/10.1007/bf02459570openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1007/s10822-009-9309-9openalex-snapshot
Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability
inferredcites →https://doi.org/10.1007/s10822-016-9938-8openalex-snapshot
Molecular graph convolutions: moving beyond fingerprints
inferredcites →https://doi.org/10.1007/s11095-015-1800-5openalex-snapshot
Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data
inferredcites →https://doi.org/10.1016/0888-7543(90)90583-gopenalex-snapshot
The National Center for Biotechnology Information
inferredcites →https://doi.org/10.1016/c2009-0-19715-5openalex-snapshot
Data Mining: Practical Machine Learning Tools and Techniques
inferredcites →https://doi.org/10.1016/j.cels.2016.01.009openalex-snapshot
TensorFlow: Biology’s Gateway to Deep Learning?
inferredcites →https://doi.org/10.1016/j.drudis.2014.10.012openalex-snapshot
Machine-learning approaches in drug discovery: methods and applications
inferredcites →https://doi.org/10.1016/j.jmgm.2007.06.005openalex-snapshot
Predicting human liver microsomal stability with machine learning techniques
inferredcites →https://doi.org/10.1016/j.jneumeth.2006.10.023openalex-snapshot
Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.003openalex-snapshot
Deep learning in neural networks: An overview
inferredcites →https://doi.org/10.1016/s1359-6446(04)03262-3openalex-snapshot
Chemoinformatics: Concepts, Methods and Tools for Drug Discovery
inferredcites →https://doi.org/10.1021/acs.jcim.5b00130openalex-snapshot
Rule-Based Prediction Models of Cytochrome P450 Inhibition
inferredcites →https://doi.org/10.1021/acs.jcim.7b00146openalex-snapshot
Is Multitask Deep Learning Practical for Pharma?
inferredcites →https://doi.org/10.1021/ci010132ropenalex-snapshot
Reoptimization of MDL Keys for Use in Drug Discovery
inferredcites →https://doi.org/10.1021/ci034160gopenalex-snapshot
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
inferredcites →https://doi.org/10.1021/ci050518sopenalex-snapshot
Classification Tree Models for the Prediction of Blood−Brain Barrier Passage of Drugs
inferredcites →https://doi.org/10.1021/ci060164kopenalex-snapshot
Random Forest Models To Predict Aqueous Solubility
inferredcites →https://doi.org/10.1021/ci5007432openalex-snapshot
Rule-Based Classification Models of Molecular Autofluorescence
inferredcites →https://doi.org/10.1021/ci500747nopenalex-snapshot
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
inferredcites →https://doi.org/10.1021/ci700142copenalex-snapshot
A Probabilistic Approach to Classifying Metabolic Stability
inferredcites →https://doi.org/10.1021/ci700351yopenalex-snapshot
Gradual in Silico Filtering for Druglike Substances
inferredcites →https://doi.org/10.1021/jm0510070openalex-snapshot
In Silico Human and RatVssQuantitative Structure−Activity Relationship Models
inferredcites →https://doi.org/10.1021/jm701407xopenalex-snapshot
Straightforward Recursive Partitioning Model for Discarding Insoluble Compounds in the Drug Discovery Process
inferredcites →https://doi.org/10.1037/h0071325openalex-snapshot
Analysis of a complex of statistical variables into principal components.
inferredcites →https://doi.org/10.1038/clpt.2009.248openalex-snapshot
Prediction of Adverse Drug Reactions Using Decision Tree Modeling
inferredcites →https://doi.org/10.1038/modpathol.3800322openalex-snapshot
Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma
inferredcites →https://doi.org/10.1088/1742-6596/1168/2/022022openalex-snapshot
An Overview of Overfitting and its Solutions
inferredcites →https://doi.org/10.1093/bioinformatics/bti721openalex-snapshot
Prediction of protein–protein interactions using random decision forest framework
inferredcites →https://doi.org/10.1093/bioinformatics/btn479openalex-snapshot
Chemical substructures that enrich for biological activity
inferredcites →https://doi.org/10.1093/bioinformatics/btv043openalex-snapshot
CypRules: a rule-based P450 inhibition prediction server
inferredcites →https://doi.org/10.1093/bioinformatics/btx350openalex-snapshot
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
inferredcites →https://doi.org/10.1093/bioinformatics/btz111openalex-snapshot
DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
inferredcites →https://doi.org/10.1093/database/baz104openalex-snapshot
DeepScreening: a deep learning-based screening web server for accelerating drug discovery
inferredcites →https://doi.org/10.1093/jac/dku457openalex-snapshot
Comprehensive physicochemical, pharmacokinetic and activity profiling of anti-TB agents
inferredcites →https://doi.org/10.1109/iccubea.2016.7860040openalex-snapshot
A review of machine learning techniques using decision tree and support vector machine
inferredcites →https://doi.org/10.1109/icimp.2010.23openalex-snapshot
Decision Tree Based on Shannon, Rényi and Tsallis Entropies for Intrusion Tolerant Systems
inferredcites →https://doi.org/10.1109/msp.2012.2205597openalex-snapshot
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
inferredcites →https://doi.org/10.1117/1.jrs.11.015020openalex-snapshot
Graph-based approach for airborne light detection and ranging segmentation
inferredcites →https://doi.org/10.1124/dmd.104.000026openalex-snapshot
EVALUATION OF FRESH AND CRYOPRESERVED HEPATOCYTES AS IN VITRO DRUG METABOLISM TOOLS FOR THE PREDICTION OF METABOLIC CLEARANCE
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1177/1087057103255988openalex-snapshot
Optimization of a Higher Throughput Microsomal Stability Screening Assay for Profiling Drug Discovery Candidates
inferredcites →https://doi.org/10.1186/1471-2105-8-25openalex-snapshot
Bias in random forest variable importance measures: Illustrations, sources and a solution
inferredcites →https://doi.org/10.1186/1471-2105-9-401openalex-snapshot
Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem
inferredcites →https://doi.org/10.1186/1758-2946-5-37openalex-snapshot
Full “Laplacianised” posterior naive Bayesian algorithm
inferredcites →https://doi.org/10.1186/s13321-017-0226-yopenalex-snapshot
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
inferredcites →https://doi.org/10.1186/s13321-017-0232-0openalex-snapshot
Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set
inferredcites →https://doi.org/10.1186/s13321-018-0286-7openalex-snapshot
Molecular generative model based on conditional variational autoencoder for de novo molecular design
inferredcites →https://doi.org/10.1371/journal.pone.0224365openalex-snapshot
Machine learning algorithm validation with a limited sample size
inferredcites →https://doi.org/10.1517/17425255.2015.980814openalex-snapshot
Applying machine learning techniques for ADME-Tox prediction: a review
inferredcites →https://doi.org/10.1517/17460441.2012.668182openalex-snapshot
Decision tree models for data mining in hit discovery
inferredcites →https://doi.org/10.2307/2986296openalex-snapshot
An Exploratory Technique for Investigating Large Quantities of Categorical Data
inferredcites →https://doi.org/10.3389/fenvs.2015.00080openalex-snapshot
DeepTox: Toxicity Prediction using Deep Learning
inferredcites →https://doi.org/10.3390/e21010037openalex-snapshot
Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.48550/arxiv.1506.05163openalex-snapshot
Deep Convolutional Networks on Graph-Structured Data
inferredcites →https://doi.org/10.48550/arxiv.1606.09375openalex-snapshot
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.5281/zenodo.1286817openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
inferredcites →https://doi.org/10.5555/1756006.1953039openalex-snapshot
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
inferredcites →https://doi.org/10.5555/2627435.2697065openalex-snapshot
Do we need hundreds of classifiers to solve real world classification problems
inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-98022-0_8openalex-snapshot
Deep Learning for In Silico ADMET Prediction
inferred← citeshttps://doi.org/10.1007/978-3-032-06066-2_18openalex-snapshot
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning
inferred← citeshttps://doi.org/10.1016/b978-0-443-33885-4.00003-2openalex-snapshot
Sculpting the future: harnessing artificial intelligence for revolutionary drug discovery
inferred← citeshttps://doi.org/10.1016/j.jpba.2021.114107openalex-snapshot
Characterization of phase I and phase II metabolites of hop (Humulus lupulus L.) bitter acids: In vitro and in vivo metabolic profiling by UHPLC-Q-Orbitrap
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00159openalex-snapshot
Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence
inferred← citeshttps://doi.org/10.1039/9781788018982-00231openalex-snapshot
Machine Learning in Drug Design
inferred← citeshttps://doi.org/10.1093/bib/bbab377openalex-snapshot
Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power
inferred← citeshttps://doi.org/10.1093/bib/bbae127openalex-snapshot
MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning
inferred← citeshttps://doi.org/10.1093/bioinformatics/btad503openalex-snapshot
CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaf524openalex-snapshot
HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning
inferred← citeshttps://doi.org/10.1109/icbiti65527.2025.11501178openalex-snapshot
Predicting Drug Absorption, Distribution, Metabolism, and Excretion (ADME)
inferred← citeshttps://doi.org/10.1109/iscait64916.2025.11010237openalex-snapshot
GSNet: Multimodal Deep Learning for Predicting Molecular Metabolic Stability
inferred← citeshttps://doi.org/10.1109/mnano.2023.3249499openalex-snapshot
Recent Advances in Quantum Computing for Drug Discovery and Development
inferred← citeshttps://doi.org/10.46254/in02.20220389openalex-snapshot
Bird’s Eye View of Machine Learning, Deep Learning in Agriculture
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