On the public citation graph

ToxMPNN: A deep learning model for small molecule toxicity prediction

0 signed · 76 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781119161110.ch4openalex-snapshot
Processing of SMILES, InChI, and Hashed Fingerprints
inferredcites →https://doi.org/10.1007/s10822-020-00310-4openalex-snapshot
Revealing cytotoxic substructures in molecules using deep learning
inferredcites →https://doi.org/10.1007/s11030-021-10217-3openalex-snapshot
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
inferredcites →https://doi.org/10.1016/j.ailsci.2021.100028openalex-snapshot
Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals
inferredcites →https://doi.org/10.1016/j.chemosphere.2020.126768openalex-snapshot
Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training
inferredcites →https://doi.org/10.1016/j.compbiomed.2023.106847openalex-snapshot
CTMLP: Can MLPs replace CNNs or transformers for COVID-19 diagnosis?
inferredcites →https://doi.org/10.1016/j.ejca.2023.02.019openalex-snapshot
Anthracycline-induced cardiotoxicity — are we about to clear this hurdle?
inferredcites →https://doi.org/10.1016/j.jhazmat.2023.131344openalex-snapshot
Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse
inferredcites →https://doi.org/10.1016/j.lfs.2021.119975openalex-snapshot
Flavonoid-statin interactions causing myopathy and the possible significance of OATP transport, CYP450 metabolism and mevalonate synthesis
inferredcites →https://doi.org/10.1016/j.scib.2020.04.006openalex-snapshot
Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction
inferredcites →https://doi.org/10.1021/acs.jcim.1c00208openalex-snapshot
Deep Learning-Based Conformal Prediction of Toxicity
inferredcites →https://doi.org/10.1021/acs.jcim.9b00237openalex-snapshot
Analyzing Learned Molecular Representations for Property Prediction
inferredcites →https://doi.org/10.1021/acs.jcim.9b00633openalex-snapshot
LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets
inferredcites →https://doi.org/10.1021/acs.jcim.9b00801openalex-snapshot
ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches
inferredcites →https://doi.org/10.1021/acs.jmedchem.1c00421openalex-snapshot
Mining Toxicity Information from Large Amounts of Toxicity Data
inferredcites →https://doi.org/10.1021/acsomega.2c05693openalex-snapshot
Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point
inferredcites →https://doi.org/10.1021/ci100050topenalex-snapshot
Extended-Connectivity Fingerprints
inferredcites →https://doi.org/10.1021/ci100176xopenalex-snapshot
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
inferredcites →https://doi.org/10.1021/ci300400aopenalex-snapshot
In silico Prediction of Chemical Ames Mutagenicity
inferredcites →https://doi.org/10.1021/es901349ropenalex-snapshot
Optimization of Bacterial Whole Cell Bioreporters for Toxicity Assay of Environmental Samples
inferredcites →https://doi.org/10.1038/s41598-017-02365-0openalex-snapshot
CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods
inferredcites →https://doi.org/10.1038/s41598-020-77766-9openalex-snapshot
SkipGNN: predicting molecular interactions with skip-graph networks
inferredcites →https://doi.org/10.1039/c7ra08415copenalex-snapshot
In silico prediction of chronic toxicity with chemical category approaches
inferredcites →https://doi.org/10.1039/c7sc02664aopenalex-snapshot
MoleculeNet: a benchmark for molecular machine learning
inferredcites →https://doi.org/10.1039/d1sc05180fopenalex-snapshot
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
inferredcites →https://doi.org/10.1080/1062936x.2018.1499125openalex-snapshot
Classification of a Naïve Bayesian Fingerprint model to predict reproductive toxicity$
inferredcites →https://doi.org/10.1081/dmr-120015694openalex-snapshot
NAPHTHALENE-INDUCED RESPIRATORY TRACT TOXICITY: METABOLIC MECHANISMS OF TOXICITY
inferredcites →https://doi.org/10.1093/bib/bbac303openalex-snapshot
Attention-wise masked graph contrastive learning for predicting molecular property
inferredcites →https://doi.org/10.1126/sciadv.aap7885openalex-snapshot
Deep reinforcement learning for de novo drug design
inferredcites →https://doi.org/10.1186/s13321-021-00570-8openalex-snapshot
Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
inferredcites →https://doi.org/10.1289/ehp.1307260openalex-snapshot
A Framework for the Next Generation of Risk Science
inferredcites →https://doi.org/10.1289/ehp.95103s7135openalex-snapshot
Environmental estrogens: health implications for humans and wildlife.
inferredcites →https://doi.org/10.17863/cam.48429openalex-snapshot
Graph Attention Networks
inferredcites →https://doi.org/10.2174/1568026618666180727152557openalex-snapshot
Applications of Machine Learning Methods in Drug Toxicity Prediction
inferredcites →https://doi.org/10.3115/v1/d14-1181openalex-snapshot
Convolutional Neural Networks for Sentence Classification
inferredcites →https://doi.org/10.3389/fchem.2018.00030openalex-snapshot
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
inferredcites →https://doi.org/10.3390/ijms23042105openalex-snapshot
VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions
inferredcites →https://doi.org/10.3390/molecules28052326openalex-snapshot
Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms
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.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1503.01445openalex-snapshot
Toxicity Prediction using Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1509.09292openalex-snapshot
Convolutional Networks on Graphs for Learning Molecular Fingerprints
inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1912.01703openalex-snapshot
PyTorch: An Imperative Style, High-Performance Deep Learning Library

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/advs.202413405openalex-snapshot
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
inferred← citeshttps://doi.org/10.1002/advs.202413786openalex-snapshot
Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning‐Based Advanced Perspectives
inferred← citeshttps://doi.org/10.1002/ddr.70182openalex-snapshot
Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development
inferred← citeshttps://doi.org/10.1002/minf.70037openalex-snapshot
An Attention‐Driven Graph Transformer With Nonlinear Modeling and Neuro‐Fuzzy Fusion for High‐Order Toxic Molecular Graph Learning
inferred← citeshttps://doi.org/10.1007/978-981-95-4821-7_24openalex-snapshot
RGFP-Net: Graph Neural Networks for Molecular Property Prediction in Drug Discovery
inferred← citeshttps://doi.org/10.1007/s11030-024-11100-7openalex-snapshot
A multiscale molecular structural neural network for molecular property prediction
inferred← citeshttps://doi.org/10.1016/b978-0-44-332908-1.00028-3openalex-snapshot
Deep learning for toxicity prediction
inferred← citeshttps://doi.org/10.1016/b978-0-443-33024-7.00004-7openalex-snapshot
Development of next-generation tools for advancing computational pharmacology and toxicology
inferred← citeshttps://doi.org/10.1016/b978-0-443-34059-8.00002-6openalex-snapshot
Integration of artificial intelligence and machine learning technologies in antiviral drug discovery: strategies and challenges
inferred← citeshttps://doi.org/10.1016/j.apsb.2026.05.028openalex-snapshot
AI and machine learning for predicting in vitro/preclinical drug toxicities and adverse events in clinical patients
inferred← citeshttps://doi.org/10.1016/j.drudis.2024.104195openalex-snapshot
Data-driven toxicity prediction in drug discovery: Current status and future directions
inferred← citeshttps://doi.org/10.1016/j.isci.2026.116937openalex-snapshot
Semantic knowledge improves molecular machine learning for chemical toxicity prediction
inferred← citeshttps://doi.org/10.1016/j.jep.2025.119480openalex-snapshot
Application and development trends of network toxicology in the safety assessment of traditional Chinese medicine
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2025.140573openalex-snapshot
Mulaqua: An interpretable multimodal deep learning framework for identifying PMT/vPvM substances in drinking water
inferred← citeshttps://doi.org/10.1016/j.lfs.2025.123821openalex-snapshot
The role of machine learning in predictive toxicology: A review of current trends and future perspectives
inferred← citeshttps://doi.org/10.1016/j.rechem.2026.103323openalex-snapshot
Novel triorganotin(IV) derivatives: Synthesis, characterization, DNA binding, ADMET prediction, hirshfeld surface analysis, molecular docking and DFT studies
inferred← citeshttps://doi.org/10.1021/acs.est.5c18927openalex-snapshot
Characterization of the Human Exposome and Risk of Reproductive Hormone Disruption by Integrating GC-HRMS Nontarget Analysis, Molecular Networking, and Knowledge Transfer Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01061openalex-snapshot
Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00171openalex-snapshot
Exploring Hidden Dangers: Predicting Mycotoxin-like Toxicity and Mapping Toxicological Networks in Hepatocellular Carcinoma
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01586openalex-snapshot
Establishment of an Integrated Model for Predicting Compound Mutagenicity with a Feature Importance Analysis
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c03214openalex-snapshot
Language of Toxicity: An eXplainable Artificial Intelligence Approach
inferred← citeshttps://doi.org/10.1021/acsinfecdis.6c00004openalex-snapshot
Machine Learning Approaches Using High-Throughput Profiling Data for Antibiotic Discovery
inferred← citeshttps://doi.org/10.1038/s41540-026-00739-7openalex-snapshot
Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty
inferred← citeshttps://doi.org/10.1039/9781837070206-00462openalex-snapshot
Machine Learning in Drug-induced Adverse Reaction Modeling: Case Studies of Drug-induced Cardiotoxicity Modeling
inferred← citeshttps://doi.org/10.1080/00268976.2026.2742292openalex-snapshot
Topology-aware directional graph representation learning for capturing structural and topological patterns in molecular toxicity graphs
inferred← citeshttps://doi.org/10.1080/10643389.2026.2693546openalex-snapshot
Artificial intelligence-powered new approach methodologies for assessing combined toxicity of chemical mixtures
inferred← citeshttps://doi.org/10.1109/prmvai70103.2026.11605645openalex-snapshot
Label-Gated Transformer for Multi-Task Molecular Toxicity Prediction
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10470980/v1openalex-snapshot
A Comprehensive Evaluation of Machine Learning Pipelines for Toxic Endpoint Prediction
inferred← citeshttps://doi.org/10.3390/toxins17100489openalex-snapshot
ProToxin, a Predictor of Protein Toxicity
inferred← citeshttps://doi.org/10.51847/tfwercxy9uopenalex-snapshot
SHAP-Guided Graph Neural Networks for Hepatotoxicity Prediction Using Molecular Substructures and Cytotoxicity Profiles
inferred← citeshttps://doi.org/10.54644/jte.2026.1954openalex-snapshot
Graph Neural Network Model for Predicting Mitochondrial Toxicity via Complementary Molecular Features
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.