In Silico Prediction of Human Intestinal Permeability (Caco-2) using QSPR Modelling for Efficient Drug Discovery.

Current drug discovery technologies Pub Date : 2025-06-16 DOI:10.2174/0115701638360381250604034810

Aayush Chowdhury, Sayantani Garai, Dipro Mukherjee, Bandita Dutta, Rina Rani Ray, Debasmita Bhattacharya, Dibyajit Lahiri, Moupriya Nag

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Abstract

Background: The quantitative structure-property relationship (QSPR) modelling can be helpful in the in-silico prediction and pre-determination of the drug permeability values of a large number of compounds through human intestinal enterocytes for screening of potential candidate drugs, thereby enhancing oral drug development.

Methods: The present study involved the development of a regression-based QSPR model for the prediction of Caco-2 cell-permeability values of compounds. The training of the model was carried out on a novel large dataset of 1272 compounds with 30 selected 2D descriptors.

Results: An R2 value of 0.96 suggested that the model was significant. Finally, the model was applied in the virtual screening of 49,430 potential compounds of the CAS database of antiviral compounds, among which the model successfully screened 100 compounds as potential leads, with 96 compounds falling within the Applicability Domain (AD).

Conclusion: The present study highlights in-silico screening, which could be beneficial for the early stages of drug development.

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利用QSPR模型预测人体肠道通透性（Caco-2），用于有效的药物发现。

背景：定量构效关系（quantitative structure-property relationship， QSPR）建模有助于大量化合物通过人肠道肠细胞的药物透性值的计算机预测和预确定，从而筛选潜在的候选药物，从而促进口服药物的开发。方法：本研究建立了一个基于回归的QSPR模型，用于预测化合物Caco-2细胞渗透率值。该模型的训练是在一个新的大型数据集上进行的，该数据集包含1272种化合物和30个选定的2D描述符。结果：R2值为0.96表明模型具有显著性。最后，将该模型应用于CAS抗病毒化合物数据库中49,430个潜在化合物的虚拟筛选，成功筛选出100个化合物作为潜在先导化合物，其中96个化合物属于应用性域（AD）。结论：本研究的重点是计算机筛选，这可能有利于药物开发的早期阶段。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Current drug discovery technologies

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