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
概括
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.
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.
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