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Beker2020 - Drug-likeness prediction based on Bayesian neural networks

ABSTRACT: To define drug-likeness, a set of 2136 approved drugs from DrugBank was taken as drug-like, and three negative datasets were selected from ZINC15 (19M), the Network of Organic Chemistry (6M) and ligands from the Protein Data Bank (13k), respectively. The drug dataset was combined with an equal subsampling of the negative dataset for each experiment, using five different molecular representations (Mold2, RDKit, MCS, EXFP4, Mol2Vec). Model Type: Predictive machine learning model. Model Relevance: Drug-likeness prediction 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/eos9sa2

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2408060002 | BioModels | 2024-08-06

REPOSITORIES: BioModels

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