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Jiménez-Luna2021 - Coloring molecules for interaction with CYP3A4

ABSTRACT: 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

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2405210003 | BioModels | 2024-05-21

REPOSITORIES: BioModels

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Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment.

Jiménez-Luna José J Skalic Miha M Weskamp Nils N Schneider Gisbert G

Journal of chemical information and modeling 20210225 3

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and <i>de novo</i> molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, hERG channel inhibition ...[more]

PMID: 33629843

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Jiménez-Luna2021 - Coloring molecules for plasma protein binding prediction

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 plasma protein binding prediction as the case for this model. MolGrad incorporates explainable features to facilitate interpretation of the predictions. In this model, they train MolGrad with data from a Plasma-protein binding assay (PPB) to predict the fraction bound in plasma of small molecules. Model Type: Predictive machine learning model. Model Relevance: Fraction of protein bound in plasma. 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/eos6ao8

2024-05-21 | MODEL2405210005 | 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 for hERG blockade

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 Pharmacokinetics predictive tasks with cardiotoxicity a case study for this model. MolGrad incorporates explainable features to facilitate interpretation of the predictions.In this model, they train MolGrad with a dataset of hERG channel blockers/non-blockers to predict the cardiotoxicity of small molecules (IC50 in hERG blockade). Model Relevance: Predicts hERG inhibition. 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/eos43at

2024-05-21 | MODEL2405210007 | BioModels

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

Rong2020 - Grover-embedding: A self-supervised Graph Neural Network for molecular representation

Project description:This is the base grover model. GROVER is a self-supervised Graph Neural Network for molecular representation pretrained with 10 million unlabelled molecules from ChEMBL and ZINC15, and has been fine-tuned to predict several activities from the MoleculeNet benchmark. Model Type: Representative machine learning model. Model Relevance: Performs embedding representation 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/eos7w6n

2024-06-05 | MODEL2406050006 | BioModels

Turon2023 - H3D virtual screening models

Project description:The H3D virtual screening cascade contains models for Mycobacterium tuberculosis and Plasmodium falciparum IC50 predictions, as well as ADME, cytotoxicity, and solubility assays. Model Type: Predictive machine learning model. Model Relevance: This panel of models provides predictions for the H3D virtual screening cascade. 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/eos7kpb

2024-05-13 | MODEL2403270001 | BioModels

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

Project description: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

2024-06-03 | MODEL2406030001 | BioModels

Gonzalez2021 - QSAR Prediction Model for CYP2C9 Inhibitor and Substrate

Project description:Analysis of metabolic stability, determining the inhibition of CYP2C9 activity and whether the compounds are a substrate for the CYP2C9 enzyme. The data used to build these models has been publicly available at PubChem (AID1645842) by ADME@NCATS. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as an inihibitor and substrate of the CYP2C9 enzymes Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos5jz9

2024-04-16 | MODEL2404160002 | BioModels

Gonzalez2021 - QSAR Prediction Model for CYP3A4 Inhibitor and Substrate

Project description:Analysis of metabolic stability, determining the inhibition of CYP3A4 activity and whether the compounds are a substrate for the CYP3A4 enzyme. The data used to build these models has been publicly available at PubChem (AID1645841) by ADME@NCATS. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as an inihibitor and substrate of the CYP3A4 enzymes. Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos3ev6

2024-05-10 | MODEL2404160001 | BioModels

Gonzalez2021 - QSAR Prediction Model for CYP2D6 Inhibitor and Substrate

Project description:Analysis of metabolic stability, determining the inhibition of CYP2D6 activity and whether the compounds are a substrate for the CYP2D6 enzyme. The data used to build these models has been publicly available at PubChem (AID1645840) by ADME@NCATS. Model Type: Predictive machine learning model. Model Relevance: Probability of inhibiting the CYP2D6 enzyme and probability of being a substrate of the enzyme Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos7nno

2024-05-10 | MODEL2404160003 | BioModels

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