使用深度图形神经网络提高了基于物理的水化自由能量预测,即使是训练集分布之外的分子
Luke H Elder1, Alexey V Onufriev1,2,3
1Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States.
The journal of physical chemistry. B
|July 11, 2025
概括
将基于物理学的模型与深度神经网络 (DNN) 结合起来,可以改善水化自由能量预测,特别是对于新型分子. 这种混合方法提高了生物分子原子模拟的准确性.
科学领域:
- 计算化学的计算化学
- 生物分子模拟
- 机器学习在科学中的应用
背景情况:
- 准确的计算水模型对于生物分子的原子模拟是必不可少的.
- 基于物理学的溶解模型通常在水化自由能量 (HFE) 预测中存在残余错误.
- 深度神经网络 (DNN) 可以与分布之外的数据作斗争,限制它们的独立应用.
研究的目的:
- 评估一个混合框架,将基于物理的模型和DNN结合起来,用于HFE预测.
- 评估框架在分布之外的数据和未见的分子支架上的表现.
- 确定使用DNN作为后处理纠正的概括性和局限性.
主要方法:
- 开发了一个解的框架,将基于经典物理的解解模型与DNN集成在一起.
- 图形神经网络架构被用来概括预测.
- 该框架使用多个数据集分割进行了评估,包括分布外的HFEs.
主要成果:
- 物理 + DNN 模型始终改进了物理模型的预测,特别是分布外数据.
- 对于分布内数据,DNN校正提高了准确性,实现根平均平方误差 (RMSE) 低于1kcal/mol.
- 当排除具有高实验不确定性的分子时,模型准确性得到改善.
结论:
- 将基于物理的模型与DNN结合起来,为提高HFE预测准确性提供了一个实用和可概括的策略.
- DNN可以作为有效的独立后处理纠正,克服独立物理或ML模型的局限性.
- 混合方法显示了改善生物分子模拟的巨大潜力.
相关概念视频
02:27Predicting Molecular Geometry
36.2K
VSEPR Theory for Determination of Electron Pair Geometries
36.2K
01:29Strength and Heat of Hydration
355
The hydration of cement is an exothermic reaction in which heat is generated as cement hydrates. This heat of hydration is critical to cement's strength development. The rate at which this heat is generated affects the temperature rise, with a majority of the heat being released early in the hydration process, half within the first three days, and about 75% within the first week.
The heat of hydration for each cement compound is significant; for instance, tricalcium aluminate (C3A) and...
The heat of hydration for each cement compound is significant; for instance, tricalcium aluminate (C3A) and...
355
01:20Maxwell-Boltzmann Distribution: Problem Solving
1.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.8K
01:26Thermodynamic Potentials
979
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
979
02:39Gibbs Free Energy
34.4K
One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
34.4K
02:42Aqueous Solutions and Heats of Hydration
15.1K
Water and other polar molecules are attracted to ions. The electrostatic attraction between an ion and a molecule with a dipole is called an ion-dipole attraction. These attractions play an important role in the dissolution of ionic compounds in water.
When ionic compounds dissolve in water, the ions in the solid separate and disperse uniformly throughout the solution because water molecules surround and solvate the ions, reducing the strong electrostatic forces between them. This process...
When ionic compounds dissolve in water, the ions in the solid separate and disperse uniformly throughout the solution because water molecules surround and solvate the ions, reducing the strong electrostatic forces between them. This process...
15.1K