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Shao2022 - Predicting HepG2 toxicity using the S2DV technique

ABSTRACT: The model uses Word2Vec, a natural language processing technique to represent SMILES strings. The model was trained on over Model Type: Predictive machine learning model. Model Relevance: Probability of HepG2 Toxicity Model Encoded by: Emmanuel Onwuegbusi(Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos2fy6

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2408070003 | BioModels | 2024-08-07

REPOSITORIES: BioModels

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Publications

S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules.

Shao Jinsong J Gong Qineng Q Yin Zeyu Z Pan Wenjie W Pandiyan Sanjeevi S Wang Li L

Briefings in bioinformatics 20220301 2

In the past few decades, chronic hepatitis B caused by hepatitis B virus (HBV) has been one of the most serious diseases to human health. The development of innovative systems is essential for preventing the complex pathogenesis of hepatitis B and reducing side effects caused by drugs. HBV inhibitory drugs have been developed through various compounds, and they are often limited by routine experimental screening and delay drug development. More recently, virtual screening of compounds has gradua ...[more]

PMID: 35062019

Similar Datasets

Shao2022 - S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules

Project description:The model uses Word2Vec, a natural language processing technique to represent SMILES strings. The model was trained on over 4000 small molecules with associated experimental HBV inhibition data (IC50) to classify compounds as HBV inhibitors or non-inhibitors. Model Type: Predictive machine learning model. Model Relevance: Predicts Probability of inhibition of HBV. Model Encoded by: Emmanuel Onwuegbusi (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos8lok

2024-06-04 | MODEL2406040001 | BioModels

Rong2020 - Grover-Toxcast: Probability of toxicity against 617 biological targets

Project description:This model has been trained using the GROVER embedding and performs prediction across the ToxCast toxicity panel, containing hundreds of toxicity outcomes, as part of the MoleculeNet benchmark. Model Type: Predicitive machine learning model. Model Relevance: Predicts Probability of toxicity against 617 biological targets. Model Encoded by: Amna Ali (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos481p

2024-06-05 | MODEL2406050002 | BioModels

Rong2020 - Grover-clintox: A classification model to predict the likelihood of failure in clinical trials due to toxicity

Project description:This model has been trained using the GROVER transformer and the Molecule Net dataset ClinTox, the authors trained a classification model to predict the likelihood of failure in clinical trials due to toxicity. The dataset has been built using FDA approved drugs (non-toxic) and a set of drugs that have failed at advanced clinical trial stages. Model Type: Predicitive machine learning model. Model Relevance: Probability that a molecule is approved by the FDA and probability that a molecule shows toxicity in clinical trials. Model Encoded by: Amna Ali (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos6fza

2024-06-05 | MODEL2406050004 | BioModels

Pu2019 - eToxPred: an ML-based approach to estimate the toxicity, and synthetic accessibility of drug candidates

Project description:The eToxPred tool has been developed to predict, on one hand, the synthetic accessibility (SA) score, or how easy it is to make the molecule in the laboratory, and, on the other hand, the toxicity (Tox) score, or the probability of the molecule of being toxic to humans. The authors trained and cross-validated both predictors on a large number of datasets, and demonstrated the method usefulness in building virtual custom libraries. Model Type: Predicitive machine learning model. Model Relevance: Predicts Synthetic Accesibility and Toxicity score of a chemical compound Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos92sw

2024-07-18 | MODEL2406270007 | BioModels

Rong2020 - Grover-tox21: Predicts activity of compounds accross the Tox21 panel.

Project description:This model has been trained using the GROVER transformer and Predicts activity of compounds in the Tox21 toxicity panel, comprising of 12 toxicity pathways, as part of the MoleculeNet benchmark datasets. Model Type: Predicitive machine learning model. Model Relevance: Toxicity measurements against 12 biological targets. Model Encoded by: Amna Ali (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos5smc

2024-06-05 | MODEL2406050005 | BioModels

Benzene toxicity

Project description:Benzene toxicity

| PRJNA641636 | ENA

mRNA expression profiles analysis of crocin mitigated triptolide-induced toxicity in mouse liver tissues

Project description:Clinical use of triptolide (TP) is restricted due to severe toxicity. This study assessed the protective effect of crocin (CR) as a natural antioxidant against TP-induced toxicity. Mouse Liver tissues were selected from the Control, MTP, and MTP+CR groups for transcriptome studies.

2022-05-06 | GSE202175 | GEO

Assaying 6xIAPP toxicity

Project description:Assaying 6xIAPP toxicity

| PRJNA411917 | ENA

MMF toxicity

Project description:MMF toxicity Raw sequence reads

| PRJNA976489 | ENA

very moderate exercise training; variations in stress and toxicity gene expression in rat hearts.

Project description:Array analysis of a set of 96 stress and toxicity genes was carried out in left ventricular cardiomyocytes from 8 control and 7 trained rats. The training protocol was very gradual and mild. Animals were sacrificed 48 hrs after the last training session in order to evaluate gene expression during the "second window of cardioprotection". Array results were validated by Real Time PCR of selected genes and Western Blot analysis of selected proteins. Plasma MDA and resistance to ischemia/reperfusion were evaluated as well. A total of 11 genes were found to be upregulated by moderate exercise, 3 to be downregulated. MDA was found to be increased in plasma of trained rats. Myocardial resistance to ischemia-reperfusion was found to be improved in trained rats with respect to controls. Results suggest that such a pattern of mild training does at the same time elicit stress responses, but also the upregulation of a number of defensive genes, that are likely to constitute the core of the adaptive mechanisms. Keywords: exercise training-related stress response and cardioprotection

2005-12-28 | GSE3904 | GEO