结合机器学习,分子动力学和自由能量分析来对 (5HT) - 2A受体调节器分类 (5HT) - 2A受体调节器分类
Xian Yu1, Yasmine Eid1, Maryam Jama1
1Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
Journal of molecular graphics & modelling
|August 16, 2024
概括
我们开发了机器学习模型来分类5-Hydroxytryptamine (5HT)-2A受体活性,达到87%的准确性. 整合分子建模揭示了药物机制,并为增强精神活性药物发现创造了新的结合能量指纹.
科学领域:
- 计算化学和药理学计算化学和药理学
- 人工智能在药物发现中的作用
- 神经科学和药物化学.
背景情况:
- 5西胺 (5HT) - 2A受体是精神活性药物开发的关键标.
- 为5HT-2A受体设计选择性化合物存在重大挑战.
- 了解5HT-2A调节器的作用机制对于有效的药物设计至关重要.
研究的目的:
- 开发和验证机器学习模型,用于对5HT-2A受体进行生物活性机制的分类.
- 将机器学习与分子建模相结合,以提高对药物相互作用的理解.
- 引入一种新的指标来评估药物对5HT-2A标的疗效.
主要方法:
- 神经网络和XGBoost机器学习模型的构建和评估.
- 应用分子建模技术,包括分子动力学模拟.
- 结合性自由能量分析以阐明药物受体相互作用.
- 为5HT-2A调节器开发一个特定的"绑定自由能量指纹".
主要成果:
- 在使用ML模型对生物活性机制进行分类时,总体准确度约为87%.
- 通过ML-MM集成,提高了模型性能,并获得了直接调制剂和前药物的机制的洞察力.
- 成功开发了一种用于5HT-2A调节器的新型"绑定自由能量指纹".
结论:
- 人工智能和结构生物学的整合为推进精神活性药物发现提供了强大的工作流.
- 开发的"结合性自由能量指纹"为评估药物疗效提供了一个新的指标.
- 这种方法对选择性5HT-2A受体调节器的合理设计具有重大前景.
关键词:
(5HT) - 2AA 在 (5HT) - 2A5 - - 氧三胺胺是一种5 - - 氧三胺.激进主义者的激进主义者敌对者的敌人 敌对者的敌人深度学习是一种深度学习.模拟MDMD的模拟神经药理学神经药理学这是一种血清胺.更多相关视频
10:21Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
2.4K
06:50Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
1.8K
相关概念视频
The Two-State Receptor Model
1.9K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
The binding affinity of a drug determines its interaction with...
1.9K
Structure-Activity Relationships and Drug Design
686
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...
686
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
