Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Wei2022 - HobPre: accurate prediction of human oral bioavailability for small molecules

ABSTRACT: HobPre predicts the oral bioavailability of small molecules in humans. It has been trained using public data on ~1200 molecules (Falcón-Cano et al, 2020, complemented with other literature and ChEMBL compounds). Model Type: Predictive machine learning model. Model Relevance: Predicts Probability of a compound having high oral bioavailability. Model Encoded by: Hellen Namulinda (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos2lqb

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406030001 | BioModels | 2024-06-03

REPOSITORIES: BioModels

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	MODEL2406030001?filename=BioModelsMetadata%20-%20eos2lqb.csv	Csv

Items per page:

1 - 1 of 1

Publications

HobPre: accurate prediction of human oral bioavailability for small molecules.

Wei Min M Zhang Xudong X Pan Xiaolin X Wang Bo B Ji Changge C Qi Yifei Y Zhang John Z H JZH

Journal of cheminformatics 20220106 1

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions ...[more]

PMID: 34991690

Similar Datasets

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

Rong2020 - Grover-qm8: Prediction of the electronic spectra and excited state energy of small molecules.

Project description:This model has been trained using the GROVER embedding with the QM8 dataset from Molecule Net, where the electronic properties have been calculated by multiple quantum mechanic methods. Model Type: Molecular properties prediction ML model. Model Relevance: Predicts electronic spectra and excited state energy of small molecules. 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/eos3xip

2024-06-04 | MODEL2406040002 | BioModels

Rong2020 - Grover-Freesolv: Experimental and calculated hydration free energy of small molecules in water

Project description:This model has been trained using the GROVER embedding and the the FreeSolv dataset from MoleculeNet. The Model is based on experimental and calculated hydration free energy of small molecules in water and Hydration free energies are relevant to understand the binding interaction between a molecule (in solution) into its binding site. Model Type: Molecular properties prediction ML model. Model Relevance: Predicts Calculated Hydration Free energy. 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/eos157v

2024-06-05 | MODEL2406050001 | BioModels

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-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-bbbp: Prediction of the Blood-brain barrier penetration.

Project description:This model predicts the Blood-Brain Barrier (BBB) penetration potential of small molecules using as training data the curated MoleculeNet benchmark containing 2000 experimental data points. It has been trained using the GROVER transformer. Model Type: Predicitive machine learning model. Model Relevance: Predicts Probability that a molecule crosses the blood brain barrier. 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/eos1amr

2024-06-05 | MODEL2406050008 | BioModels

Chi2019 - In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Project description:The authors provide a dataset of 200 small molecules and their experimentally measured permeability in a PAMPA assay. Using this data, we have trained a model that predicts the logarithm of the effective permeability coefficient. Model Type: Predictive machine learning model. Model Relevance: Predicts pampa-permeability. 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/eos97yu

2024-06-03 | MODEL2406030003 | BioModels

Liu2023 - Acinetobacter baumannii growth inhibition prediction with Deep Learning

Project description:This model is a Chemprop neural network trained with a growth inhibition dataset. Authors screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. They discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of growth inhibition of the bacteria A. Baumannii. 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/eos3804

2024-05-28 | MODEL2405080001 | BioModels

Jiménez-Luna2021 - Coloring molecules for Caco-2 cell permeability

Project description:By combining a Message-Passing Graph Neural Network (MPGNN) and a Forward fully connected Neural Network (FNN) with an integrated gradients explainable artificial intelligence (XAI) method, the authors developed MolGrad and tested it on a number of ADME predictive tasks such as pCaco-2 cell permeability as the case for this model. MolGrad incorporates explainable features to facilitate interpretation of the predictions. This model has been trained using experimental data on the permeability of molecules across Caco2 cell membranes (Papp, cm s-1). Model Type: Predictive machine learning model. Model Relevance: Predicts Log10 of the Passive permeability in cm s-1. 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/eos1af5

2024-05-21 | MODEL2405210006 | BioModels

Jiménez-Luna2021 - Coloring molecules for interaction with CYP3A4

Project description:By combining a Message-Passing Graph Neural Network (MPGNN) and a Forward fully connected Neural Network (FNN) with an integrated gradients explainable artificial intelligence (XAI) method, the authors developed MolGrad and tested it on a number of ADME predictive tasks such as metabolism as the case for this model. MolGrad incorporates explainable features to facilitate interpretation of the predictions. This model has been trained using a ChEMBL dataset of CYP450 3A4 inhibitors (0) and non-inhibitors (1). Model Type: Predictive machine learning model. Model Relevance: Probability that the molecule is metabolized by Cyp3A4. 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/eos96ia

2024-05-21 | MODEL2405210003 | BioModels