黄金发的范式:比较经典的机器学习,大型语言模型,和药物发现应用程序的几次射击学习
Scott H Snyder1, Patricia A Vignaux1, Mustafa Kemal Ozalp1
1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
Communications chemistry
|June 12, 2024
概括
选择合适的机器学习模型取决于您的数据. 短暂学习在小数据集,变压器在多种中等数据集和古典模型在大数据集中表现出色.
科学领域:
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 像变压器 (大型语言模型,LLM) 和少数射击学习 (FSLC) 这样的机器学习 (ML) 模型显示出希望,但"没有免费午餐"定理意味着没有单一的算法是普遍最好的.
- 经典的ML,FSLC和变压器模型有不同的优缺点,取决于任务.
研究的目的:
- 评估各种数据集大小和多样性级别的经典 (SVR),FSLC和变压器 (MolBART) 模型的性能.
- 根据数据集特征确定最佳的ML模型策略.
主要方法:
- 对支持向量回归 (SVR),少数射击学习 (FSLC) 和变压器 (MolBART) 模型进行比较分析.
- 在大小 (小,中,大) 和多样性 (特征分布) 不同的数据集上测试模型性能.
主要成果:
- 在小数据集 (<50个分子) 上,FSLC模型的表现优于其他模型.
- 变压器在各种小到中型数据集 (50-240分子) 上表现出卓越的性能.
- 经典的ML模型在大型,足够大小的数据集上表现最好.
结论:
- 最佳的ML模型选择取决于数据集的大小和多样性,为每个模型类型定义一个"金髮区".
- 这项研究为科学应用中的新数据集选择适当的ML算法提供了指导.
相关概念视频
Drug Discovery: Overview
7.8K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
7.8K
Pharmacokinetic Models: Comparison and Selection Criterion
66
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
66
Analysis of Population Pharmacokinetic Data
252
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
252
Pharmacokinetic Models: Overview
653
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
653
Structure-Activity Relationships and Drug Design
699
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.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
699
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
68
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
68


