机器学习用于小分子药物发现的未来将由数据驱动
Guy Durant1, Fergus Boyles1, Kristian Birchall2
1Department of Statistics, University of Oxford, Oxford, UK.
Nature computational science
|October 15, 2024
概括
专注于药物发现中的机器学习模型的数据质量,而不仅仅是先进的算法,是改善治疗开发的关键. 更好的数据将推动在发现新的小分子药物方面取得重大进展.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 生物技术是生物技术.
背景情况:
- 机器学习 (ML) 在小分子疗法开发中的整合被广泛预期将加速药物发现.
- 尽管在ML算法和架构方面取得了进展,但治疗开发结果的实质性改进是有限的.
研究的目的:
- 建议加强对训练和对ML模型的基准测试数据质量的重视,对于药物发现的未来进展至关重要.
- 探索研究途径和战略,以解决ML驱动药物开发中的数据相关挑战.
主要方法:
- 这种观点综合了当前应用机器学习到药物发现的趋势和挑战.
- 它强调从算法复杂性转向以数据为中心的模型开发和验证方法.
主要成果:
- 目前在药物发现中的ML应用显示,只有算法改进的回报正在减少.
- 以数据为中心的方法被认为是提高模型性能和可靠性的更有前途的策略.
结论:
- 优先考虑高质量,精心策划的数据集进行培训和基准测试对于实现机器学习在小分子治疗中的全部潜力至关重要.
- 未来的研究应该专注于数据生成,标准化和创新方法,以克服ML驱动药物发现中的数据限制.
相关概念视频
Drug Discovery: Overview
7.6K
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...
7.6K
Structure-Activity Relationships and Drug Design
668
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...
668
MALDI-TOF Mass Spectrometry
4.7K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
4.7K
Ligand Binding Sites
12.8K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.8K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
98
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
98


