Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types}) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process -- spanning chemical perception to parameter assignment -- is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn and extend existing molecular mechanics force fields and construct entirely new force fields applicable to both biopolymers and small molecules from quantum chemical calculations, and even learn to accurately predict free energies from experimental observables. This approach is implemented in the free and open source package Espaloma, available at https://github.com/choderalab/espaloma.
翻译:分子机(MM) 潜力长期以来一直是计算化学的工马。 利用精确性和速度,这些功能形式在生物分子模型和药物发现的广泛应用中,从快速虚拟筛选到详细的自由能源计算,在生物分子模型和药物发现中,从快速虚拟筛选到详细的自由能源计算,都发现使用多种应用。 传统上,MM潜力依靠人类精细、僵硬和不易伸缩的离散化学感知规则(原子类型 ) 来应用小分子或生物聚合物的参数,使得很难优化类型和参数以适应量子化学或物理属性数据。 在这里,我们提出一种替代方法,即利用图形神经网络来观察化学环境,产生连续的原子嵌入,从中预测数值和非阳性参数,利用变化性保存层。 由于所有阶段都是从光滑的神经功能构建的,将化学感知到参数任务分配的整个过程都是模块和端到端到端到端到端到端到端,对于模型参数来说,使得新的力场易于构建、扩展和应用到任意分子。 我们表明,这一方法不仅在可充分表达的直观到在可复制的分子/ 分子模型和分子模型中,并且从可应用的物理的化学模型中学习到可以应用的物理物理物理和分子流和分子的物理物理的物理的物理物理物理学系。