Research graph
References from Shiny binary modeling platform: a reproducibility-oriented software platform for structured binary modeling with integrated interpretation, validation support, model comparison, and archive export. Local targets link to admitted publications; unresolved targets remain external evidence.
Overcoming barriers to machine learning applications in toxicity prediction
10.1080/17425255.2023.2294939 · 2024 · External reference
Computational determination of toxicity risks associated with a selection of approved drugs having demonstrated activity against COVID-19
10.1186/s40360-021-00519-5 · 2021 · External reference
Machine learning toxicity prediction: latest advances by toxicity end point
10.1021/acsomega.2c05693 · 2022 · External reference
Shiny: web application framework for R
2026 · External reference
In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities
10.1186/s40360-021-00499-6 · 2021 · External reference
toxCSM: comprehensive prediction of small molecule toxicity profiles
10.1093/bib/bbac337 · 2022 · External reference
ToxinPredictor: computational models to predict the toxicity of molecules
10.1016/j.chemosphere.2024.143900 · 2025 · External reference
admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties
10.1093/nar/gkae298 · 2024 · External reference
XML-CIMT: explainable machine learning model for predicting chemical-induced mitochondrial toxicity
10.3390/ijms232415655 · 2022 · External reference
ToxDAR: a workflow software for analyzing toxicologically relevant proteomic and transcriptomic data, from data preparation to toxicological mechanism elucidation
10.3390/ijms25179544 · 2024 · External reference
Understanding predictions of drug profiles using explainable machine learning models
10.1186/s13040-024-00378-w · 2024 · External reference
Mime: a flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection
10.1016/j.csbj.2024.06.035 · 2024 · External reference
A unified approach to interpreting model predictions
2017 · External reference
DeepTox: toxicity prediction using deep learning
10.3389/fenvs.2015.00080 · 2016 · External reference
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
10.1186/s40360-018-0282-6 · 2019 · External reference
TCMSP: a database of systems pharmacology for drug discovery from herbal medicines
10.1186/1758-2946-6-13 · 2014 · External reference
Ten simple rules for reproducible computational research
10.1371/journal.pcbi.1003285 · 2013 · External reference
Machine learning for toxicity prediction using chemical structures: pillars for success in the real world
10.1021/acs.chemrestox.5c00033 · 2025 · External reference
Unresolved reference
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Unresolved reference
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Machine learning in predictive toxicology: recent applications and future directions for classification models
10.1021/acs.chemrestox.0c00316 · 2021 · External reference
ToxicR: a computational platform in R for computational toxicology and dose-response analyses
10.1016/j.comtox.2022.100259 · 2023 · External reference
Best practices for scientific computing
10.1371/journal.pbio.1001745 · 2014 · External reference
ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
10.1093/nar/gkab255 · 2021 · External reference
admetSAR 2.0: web service for prediction and optimization of chemical ADMET properties
10.1093/bioinformatics/bty707 · 2019 · External reference
In silico toxicity studies of traditional Chinese herbal medicine: a mini review
10.1016/j.sbi.2023.102588 · 2023 · External reference
