Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Li2021 - HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors

ABSTRACT: The model predicts the inhibitory potential of small molecules against Histone deacetylase 3 (HDAC3), a relevant human target for cancer, inflammation, neurodegenerative diseases and diabetes. The authors have used a dataset of 1098 compounds from ChEMBL and validated the model using the benchmark MUBD-HDAC3. Model Type: Predictive machine learning model. Model Relevance: Probability that the molecule is a HDAC3 inhibitor 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/eos1n4b

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406210001 | BioModels | 2024-08-06

REPOSITORIES: BioModels

ACCESS DATA

Json Xml

Publications

HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors.

Li Shan S Ding Yu Y Chen Miaomiao M Chen Ya Y Kirchmair Johannes J Zhu Zihao Z Wu Song S Xia Jie J

Molecular informatics 20201123 3

Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features f ...[more]

PMID: 33067876

Similar Datasets

Pereira2020 - Discovering growth Inhibitors of Neisseria gonorrhoeae with Machine Learning

Project description:The authors curated a dataset of 282 compounds from ChEMBL, of which 160 (56.7%) were labeled as active N. gonorrhoeae inhibitor compounds. They used this dataset to build a naïve Bayesian model and used it to screen a commercial library. With this method,they identified and validated two hits compound. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of activity for the inhibition of the Antimicrobial pathogen N. gonorrhoeae. 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/eos5cl7

2024-05-08 | MODEL2405080003 | 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

Wong2024 - Discovery of a structural class of antibiotics with explainable deep learning

Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of growth inhibition. 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/eos18ie

2024-05-08 | MODEL2405080002 | BioModels

Wong2024 - Cytotoxicity Prediction from Human Cell Lines

Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci. Model Type: Predictive machine learning model. Model Relevance: Prediction of Human cytotoxicity endpoints. 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/eos42ez

2024-05-13 | MODEL2405130002 | 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

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

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-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

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

Machine learning-accelerated computational fluid dynamics.

Project description:Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large-eddy simulation, our results are as accurate as baseline solvers with 8 to 10× finer resolution in each spatial dimension, resulting in 40- to 80-fold computational speedups. Our method remains stable during long simulations and generalizes to forcing functions and Reynolds numbers outside of the flows where it is trained, in contrast to black-box machine-learning approaches. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization.

| S-EPMC8166023 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data