相关实验视频

Updated: Sep 19, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

DTBA-net:使用混合CNN模型中的特征选择来预测药物-目标绑定亲和力

Priya Mishra¹, Swati Vipsita²

¹Department of Computer Science and Engineering, IIIT Bhubaneswar, Odisha, India. c121008@iiit-bh.ac.in.

Journal of computer-aided molecular design

|June 16, 2025

概括

新型混合神经网络DTBA-Net提高了药物向结合亲和力 (DTBA) 预测的准确性和效率. 该模型将最佳特征选择与CNN集成在一起,通过增强预测能力来加速药物发现.

科学领域:

计算化学是一种计算化学.
生物信息学是一种生物信息学.
药物发现药物发现

背景情况:

准确的药物标结合亲和力 (DTBA) 预测对于虚拟查和药物重新定位至关重要.
在DTBA预测中的挑战包括有限的数据,高维生化学数据和异质数据源.
现有的深度学习框架与这些复杂性作斗争.

研究的目的:

开发一种新的混合神经网络模型,DTBA-Net,以提高DTBA预测的准确性和效率.
为了解决当前处理复杂生化数据的方法在DTBA预测的局限性.

主要方法:

DTBA-Net使用混合卷积神经网络 (CNN) 架构.
蛋白质序列和化合物结构通过CNN进行处理,包括卷积层,平面层和密集块.
使用修改后的JAYA算法集成了一个优化的功能选择过程,以减少计算开销和提高性能.

主要成果:

在基准数据集上,DTBA-Net实现了高精度,包括在DAVIS数据集上的R平方值为0.95和平均绝对误差 (MAE) 为0.17.
与药物Nirmatrelvir的进一步验证产生了0.96的R平方值,证明了强度和可扩展性.
优化特征选择的整合加速了模型训练,提高了预测准确度.

结论:

DTBA-Net为DTBA预测提供了一个可扩展,高效和准确的解决方案.
该模型的性能促进了更快,更可靠的药物发现过程.
在推进计算药物发现方法学方面,DTBA-Net显示出巨大的潜力.

关键词:

卷积神经网络是一种卷积神经网络.药物标结合亲和力药物标结合亲和力功能选择功能选择修改了JAYA算法的修改.虚拟选是一个虚拟的选.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

相关概念视频

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Drug-Receptor Bonds

Drug-Receptor Bonds

Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...

Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...