Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years,Journal of Chemical Information and Modeling

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Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-08-13 , DOI: 10.1021/acs.jcim.4c00747
Afnan Sultan ₁ , Jochen Sieg ₂ , Miriam Mathea ₂ , Andrea Volkamer ₁

Affiliation

Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science. Over the last decades, diverse computational techniques have been developed, from using simple physical and chemical properties and molecular fingerprints in statistical models and classical machine learning to advanced deep learning approaches. In this review, we aim to distill insights from current research on employing transformer models for MPP. We analyze the currently available models and explore key questions that arise when training and fine-tuning a transformer model for MPP. These questions encompass the choice and scale of the pretraining data, optimal architecture selections, and promising pretraining objectives. Our analysis highlights areas not yet covered in current research, inviting further exploration to enhance the field’s understanding. Additionally, we address the challenges in comparing different models, emphasizing the need for standardized data splitting and robust statistical analysis.

中文翻译：

分子特性预测的变形金刚：过去五年的经验教训

分子特性预测 (MPP) 对于药物发现、作物保护和环境科学至关重要。在过去的几十年里，已经开发了多种计算技术，从在统计模型和经典机器学习中使用简单的物理和化学特性和分子指纹到先进的深度学习方法。在这篇综述中，我们的目标是从当前关于采用 MPP 变压器模型的研究中提炼出见解。我们分析了当前可用的模型，并探讨了在训练和微调 MPP 变压器模型时出现的关键问题。这些问题包括预训练数据的选择和规模、最佳架构选择以及有希望的预训练目标。我们的分析突出了当前研究尚未涵盖的领域，欢迎进一步探索以增强对该领域的理解。此外，我们还解决了比较不同模型的挑战，强调标准化数据分割和稳健统计分析的需要。

更新日期：2024-08-13

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