Barlow Twins deep neural network for advanced 1D drug–target interaction prediction

IF 5.7 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber
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引用次数: 0

Abstract

Accurate prediction of drug–target interactions is critical for advancing drug discovery. By reducing time and cost, machine learning and deep learning can accelerate this laborious discovery process. In a novel approach, BarlowDTI, we utilise the powerful Barlow Twins architecture for feature-extraction while considering the structure of the target protein. Our method achieves state-of-the-art predictive performance against multiple established benchmarks using only one-dimensional input. The use of our hybrid approach of deep learning and gradient boosting machine as the underlying predictor ensures fast and efficient predictions without the need for substantial computational resources. We also propose the use of an influence method to investigate how the model reaches its decision based on individual training samples. By comparing co-crystal structures, we find that BarlowDTI effectively exploits catalytically active and stabilising residues, highlighting the model’s ability to generalise from one-dimensional input data. In addition, we further benchmark new baselines against existing methods. Together, these innovations improve the efficiency and effectiveness of drug–target interactions predictions, providing robust tools for accelerating drug development and deepening the understanding of molecular interactions. Therefore, we provide an easy-to-use web interface that can be freely accessed at https://www.bio.nat.tum.de/oc2/barlowdti.

Our computationally efficient and effective hybrid approach, combining the deep learning model Barlow Twins and gradient boosting machines, outperforms state-of-the-art methods across multiple splits and benchmarks using only one-dimensional input. Furthermore, we advance the field by proposing an influence method that elucidates model decision-making, thereby providing deeper insights into molecular interactions and improving the interpretability of drug-target interactions predictions.

Barlow Twins深度神经网络用于一维药物-靶标相互作用预测
准确预测药物-靶标相互作用对于推进药物发现至关重要。通过减少时间和成本,机器学习和深度学习可以加速这一费力的发现过程。在一种新颖的方法BarlowDTI中,我们利用强大的Barlow Twins架构进行特征提取,同时考虑目标蛋白的结构。我们的方法仅使用一维输入就可以针对多个已建立的基准实现最先进的预测性能。使用我们的深度学习和梯度增强机器的混合方法作为底层预测器,确保快速有效的预测,而不需要大量的计算资源。我们还建议使用影响方法来研究模型如何基于单个训练样本达成决策。通过比较共晶结构,我们发现BarlowDTI有效地利用了催化活性和稳定残基,突出了模型从一维输入数据进行推广的能力。此外,我们进一步对现有方法的新基线进行基准测试。总之,这些创新提高了药物-靶标相互作用预测的效率和有效性,为加速药物开发和加深对分子相互作用的理解提供了强大的工具。因此,我们提供了一个易于使用的web界面,可以在https://www.bio.nat.tum.de/oc2/barlowdti上自由访问。我们的计算效率和有效的混合方法,结合了深度学习模型Barlow Twins和梯度增强机器,在多个分割和仅使用一维输入的基准测试中优于最先进的方法。此外,我们通过提出一种影响方法来阐明模型决策,从而对分子相互作用提供更深入的见解,并提高药物-靶标相互作用预测的可解释性,从而推动了该领域的发展。
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来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
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