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

ABSTRACT: 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

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406050006 | BioModels | 2024-06-05

REPOSITORIES: BioModels

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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

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

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

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

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

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Rong2020 - Grover-sider: Prediction of the putative adverse drug reactions (ADR) of a molecule

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