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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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将域名知识与微调的大型语言模型融合在一起,用于增强分子性质预测.

Liangxu Xie1, Yingdi Jin2, Lei Xu1

  • 1Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou 213001, China.

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此摘要是机器生成的。

本研究引入了双模学习 (KFLM2) 的知识融合大语言模型,以增强药物发现中的分子性质预测. 将领域知识与LLM集成,提高了准确性,可能会彻底改变药物开发.

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科学领域:

  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现
  • 人工智能的人工智能

背景情况:

  • 大型语言模型 (LLM) 在科学应用中表现有前途,但在分子性质预测方面存在困难.
  • 现有的化学专用LLM在这个关键的药物发现任务中没有取得令人满意的表现.

研究的目的:

  • 通过将深入的领域知识整合到法学士课程中,提高分子性质预测的准确性.
  • 开发一种新的双模学习方法,以改善药物发现预测.

主要方法:

  • 微调的DeepSeek-R1-Distill-Qwen-1.5B使用ZINC和ChEMBL数据集来获得SMILES嵌入.
  • 集成的LLM衍生SMILES嵌入式与分子图表表示.
  • 训练了一种混合神经网络,使用组合双模态输入来进行财产预测.

主要成果:

  • 双模式的知识融合大语言模型 (KFLM2) 在十个回归和分类数据集中的九个实现了更高的预测性能.
  • 视觉化证实,将LLM嵌入与分子图的结合提供了互补信息,提高了预测准确度.
  • 模型的性能不仅取决于尺寸,还取决于预训练和微调的有效知识整合.

结论:

  • 将领域知识集成到LLM中是一种合理和有效的策略,用于精确的分子性质预测.
  • 拟议的KFLM2方法为彻底改变药物开发和发现过程提供了重大进展.
  • 双模学习结合了LLM嵌入和分子图表,增强了超越单模方法的预测能力.