Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT: 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

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406050004 | BioModels | 2024-06-05

REPOSITORIES: BioModels

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	MODEL2406050004?filename=BioModelsMetadata%20-%20eos6fza.csv	Csv

Items per page:

1 - 1 of 1

Similar Datasets

Machine Learning Identifies Candidates for Drug Repurposing in Alzheimer's Disease

Project description:Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. In this study, we profile 80 FDA-approved and clinically tested drugs in neural cell cultures, with the goal of producing a ranked list of possible repurposing candidates.

2021-01-14 | GSE164788 | GEO

Rahman2022 - High throughput antibacterial screening with machine learning.

Project description:Prediction of antimicrobial potential using a dataset of 29537 compounds screened against the antibiotic resistant pathogen Burkholderia cenocepacia. The model uses the Chemprop Direct Message Passing Neural Network (D-MPNN) and has an AUC score of 0.823 for the test set. It has been used to virtually screen the FDA approved drugs as well as a collection of natural product list (>200k compounds) with hit rates of 26% and 12% respectively. Model Type: Predictive machine learning model. Model Relevance: Probability that a compound inhibits bacterial pathogens with a focus on ESKAPE. Model Encoded by: Sarima Chiorlu (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos5xng

2024-04-22 | MODEL2404080002 | BioModels

Pemetrexed and gemcitabine as combination therapy for the treatment of Group3 medulloblastoma

Project description:We devised a high-throughput, cell-based assay to identify novel therapeutic compounds to treat Group3 medulloblastoma (G3 MB). Mouse G3 MBs, grown as neurospheres, were screened against a library of approximately 7,000 compounds including FDA-approved drugs. We identified two FDA-approved drugs, pemetrexed and gemcitabine that preferentially inhibited tumor proliferation in vitro compared to control neurospheres, and substantially inhibited tumor proliferation in vivo. When combined, the two drugs significantly increased survival P7 Trp53-/-, Cdkn2c-/- neurospheres and MYC mouse cells were compared treated and untreated with drugs pemetrexed+gemcitabine

2014-04-15 | E-GEOD-46406 | biostudies-arrayexpress

Drug repurposing for the treatment of HIV-Associated Neurocognitive Disorder by AI-based literature mining

Project description:HIV-1 Associated Neurocognitive Disorder (HAND) is a common and clinically detrimental complication of HIV infection. Viral proteins including Tat, released from infected cells, cause neuronal toxicity. Substance abuse in HIV-infected patients greatly exacerbates the severity of neuronal damage. To repurpose small molecule inhibitors for anti-HAND therapy, we employed MOLIERE, an AI-based literature mining system that we developed. All human genes were analyzed and prioritized by MOLIERE to find previously unknown targets connected to HAND. The list was narrowed to those with known small molecule inhibitors developed for other applications and lacking systemic toxicity in animal models. We tested the activity of small molecules targeted against the proteins of five prioritized genes to protect against the combined neurotoxicity of HIV-Tat and cocaine in primary neuronal cultures. Four prevented Tat and cocaine toxicity. The compounds are: the FDA approved drugs Amlexanox and Tazemetostat (EPZ-6438), Itaconate and Senicapoc. Despite the disparate molecular targets of these drugs, analysis revealed a common mechanism of neuroprotection; namely that modulation of astrocyte and microglia status prevents the toxicity of Tat and cocaine.

2024-10-30 | GSE269380 | GEO

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

Identification of FDA-Approved Drugs that Induce Heart Regeneration in Mammals

Project description:Identification of FDA-Approved Drugs that Induce Heart Regeneration in Mammals

| PRJNA1004155 | ENA

Rong2020 - Grover-Lipo: Octanol/water distribution coefficient

Project description:This model has been trained using the GROVER embedding with the Lipophilicity Molecule Net dataset. Model Type: Molecular properties prediction ML model. Model Relevance: Predicts octanol/water distribution coefficient. 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/eos85a3

2024-06-04 | MODEL2406040004 | BioModels

Rong2020 - Grover-Esol: Prediction of water solubility (log solubility in mols per litre) for common organic small molecules

Project description:This model has been trained using the GROVER embedding and the Molecule Net ESOL dataset. . Model Type: Molecular properties prediction ML model. Model Relevance: Predicts Log Solubility. 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/eos8451

2024-06-04 | MODEL2406040005 | BioModels

Rong2020 - Grover-QM7: Atomization energy prediction of small molecules.

Project description:This model has been trained using the GROVER embedding with the QM7 dataset from MoleculeNet, a subset of GDB13 containing all molecules up to 23 atoms (7 heavy atoms + C, S, O, N). This dataset contains the computed atomization energy of 7165 molecules. Model Type: Molecular properties prediction ML model. Model Relevance: Predicts the atomization energy of a molecule. 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/eos6o0z

2024-06-04 | MODEL2406040003 | BioModels

Pemetrexed and gemcitabine as combination therapy for the treatment of Group3 medulloblastoma

2014-04-15 | GSE46406 | GEO