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The state-of-the-art machine learning model for Plasma Protein Binding Prediction: computational modeling with OCHEM and experimental validation

Zenodo2024-07-04 更新2026-04-07 收录

免疫球蛋白结合预测

药物计算机辅助筛选

数据链接：

https://zenodo.org/doi/10.5281/zenodo.12641856 数据链接链接失效反馈

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资源简介：

Institute of Materia Medica, Chinese Academy of Medical Sciences purchased 10,000 ChemDiv databases.

应用场景：

创建时间：

2024-07-04

表位设计

免疫球蛋白结合预测

Table S2. Classification Test Binders. Set of binders in the test set including computational analysis. The peptides in Column A represent the test data set, sorted by Column B. The highest measured v

Figshare2016-12-15 更新60

Number of binding/non-binding peptides in IEDB benchmark dataset.

免疫球蛋白结合预测

表位识别基准

UPDS refers to datasets of non-redundant peptides. The last three columns refer to similarity-reduced datasets (see text for details).

NIAID Data Ecosystem50

Performance of the classifiers illustrated by confusion matrices of the prediction of IVIG binding on the test set.

免疫球蛋白结合预测

分类器性能评估

aTraining sets consist of either three times more “non-binding” than “binding” peptides (original) or an equal number of both groups (balanced).bOur classifiers (ML-advanced, PWM with treshold 2.45; s

Figshare2015-12-02 更新10

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability

免疫球蛋白结合预测

QSAR建模

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data

NIAID Data Ecosystem60

Additional file 4 of A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin

线性表位设计

免疫球蛋白结合预测

Table S4. Designed peptides and their experimental validation. Data set of the designed peptides including computational analysis. The peptides in Column A were further categorized as outlined in Mate

NIAID Data Ecosystem30

The state-of-the-art machine learning model for Plasma Protein Binding Prediction: computational modeling with OCHEM and experimental validation

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