Beker2020 - Drug-likeness prediction based on Bayesian neural networks

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

为界定药物相似性（drug-likeness），本研究选取药物银行（DrugBank）数据库中的2136种已获批药物作为类药物样本，并分别从ZINC15（1900万条数据）、有机化学网络（Network of Organic Chemistry）数据库（600万条数据）以及蛋白质数据银行（Protein Data Bank, PDB）的配体数据（1.3万条数据）中构建三类负样本数据集。针对每项实验，本研究将药物数据集与经等量下采样后的负样本数据集进行合并，并采用五种不同的分子表征方式：Mold2、RDKit、MCS、EXFP4及Mol2Vec。

模型类型：预测型机器学习模型。
模型适用场景：药物相似性预测。
模型编码实现：Amna Ali（Ersilia）。
元数据由Zainab Ashimiyu-Abdusalam提交至BioModels数据库。

本模型的代码实现由Ersilia团队开源，开源地址为：https://github.com/ersilia-os/eos9sa2