There are 3 primary theoretical models used to simulate atoms and molecules with computers. In decreasing order of their computational cost, these models are quantum mechanics (QM), molecular mechanics (MM), and coarse-grained (CG). Computational chemistry research literature is rife with examples that utilize high performance computing (HPC) to scale these models to large and relevant problems at each individual scale. However, the grand challenge lies in effectively bridging these scales, both spatially and temporally, to study richer chemical models that go beyond single-scale physics. This talk describes the state of the art in multiscale computational chemistry with an eye towards improving software developer productivity on upcoming exascale architectures, where we require productive programming environments, enhanced support for coupled scientific workflows, adaptive and introspective runtime systems, resilience to hardware failures, and extreme scalability.