SIMULATIONS OF BIOMOLECULES

人工神经网络被引入开发一种新的形式 基于势能的氢键势 水-水和肽-水体系的表面（PES）。 的潜在 能量函数有三项，代表静电 相互作用、非键相互作用和极化。 第一 两项，经典的1-6-12成对势能函数， 采用 由于极化不是原子对的性质，因此人工 神经网络已被应用于描述复杂的 势能面代替解析两两位势 能量功能。 为了计算水的净原子电荷， 电势导出电荷（PD）方法已应用于一些 约束 从该方法的收费显示良好的协议与ab 初始静电势，也显示出显着的协议与 与其他方法相比，实验偶极子和四极矩 这是由其他人开发的。 为了测试势能 功能，分子动力学和蒙特卡罗模拟将 执行。 从模拟中，一些物理性质（热 容量、汽化热和径向分布函数. 各种温度）将被计算并与 实验数据 有机或生物小分子的水合能 可以用这个电势计算，然后与 实验数据 同时，最低能耗的水结构 二聚体，三聚体和四聚体将计算与此潜力 函数，并与实验数据进行比较。

Artificial Neural Networks were introduced to develop a new form for the hydrogen bond potential based on the potential energy surfaces(PES) of water-water and peptide-water systems. The potential energy function has three terms which represent electrostatic interaction, nonbonded interaction and polarization. For the first two terms, classical 1-6-12 pairwise potential energy functions were used. Since the polarization is not an atom pair property, artificial Neural Networks have been applied to describe the complicated potential energy surface instead of analytical pairwise potential energy functions. To calculate the net atomic charges of water, a Potential Derived Charge(PD) method has been applied with some constraints. The charges from the method show good agreement with ab initio electrostatic potentials and also show prominent agreement with experimental dipole and quadrupole moments compared to other methods which have been developed by others. To test the potential energy function, Molecular Dynamics and Monte Carlo simulations will be performed. From the simulations, some physical properties (heat capacity, heat of vaporization and radial distribution function at various temperature) will be calculated and will be compared to experimental data. Hydration energy of organic or small biomolecules can be calculated with this potential and then compared to experimental data. Also minimum-energy structures for the water dimer, trimer and tetramer will be calculated with this potential function and compared to experimental data.