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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Aminoacyl-tRNA synthetases are present in both eukaryotes and bacteria. Though eukaryotes have 20 different aminoacyl-tRNA synthetases to couple to 20 amino acids, many bacteria do not have genes for all of these aminoacyl-tRNA synthetases. Despite this, they still use all 20 amino acids to synthesize their proteins. For instance, some bacteria do not have the gene encoding the enzyme that couples glutamine with its partner tRNA. In these organisms, one enzyme adds glutamic acid to all of the...
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Updated: Jul 27, 2025

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
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Published on: April 26, 2024

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适应性语言模型培训用于分子设计.

Andrew E Blanchard1, Debsindhu Bhowmik2, Zachary Fox1

  • 1Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.

Journal of cheminformatics
|June 8, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了适应性语言模型,用于药物发现中的加速分子设计. 与固定模型相比,自适应策略显著提高了分子优化,有助于发现类似药物和可合成化合物.

关键词:
药物发现 药物发现遗传算法 遗传算法 遗传算法蒙面语言模型模型

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科学领域:

  • 计算化学是一种计算化学.
  • 人工智能在药物发现中的作用
  • 机器学习用于分子设计.

背景情况:

  • 庞大的化学空间需要计算方法来实现高效的分子设计.
  • 基因算法和掩面语言模型 (MLM) 用于自动化分子生成和突变.
  • MLM从大型图书馆中学习化学序列,以预测分子重组.

研究的目的:

  • 研究适应性语言模型,以改善药物发现中的分子生成.
  • 为了比较固定的 (预训练) 和适应性的 (再训练) MLM 策略来进行分子优化.
  • 评估适应性训练对优化药物相似性,合成性和结合性亲和性的影响.

主要方法:

  • 开发并比较了两个语言模型策略:固定和自适应.
  • 固定策略使用预训练的MLM来产生分子突变.
  • 适应性策略将MLM重新训练在具有所需性质的新生成分子上.

主要成果:

  • 适应性策略使语言模型能够更好地适应人口的分子分布.
  • 与固定预训练模型相比,适应性训练显著改善了健身优化.
  • 证明了药物相似性,合成性和预测的蛋白质结合亲和力的改进优化.

结论:

  • 适应性语言模型为分子设计和优化任务提供了显著的优势.
  • 建议采用混合方法,最初使用固定策略,然后采用自适应策略,以提高健身优化.
  • 这项工作使语言模型的应用在加速药物发现管道中的应用成为可能.