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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

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SUBMITTER: Zainab Ashimiyu-Abdusalam 

PROVIDER: MODEL2408060002 | biostudies-other |

SECONDARY ACCESSION(S): STATO:0000274

REPOSITORIES: biostudies-other

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