Molecular binding rate constants are important parameter for assessing
drug efficacy in vivo. In this work, we present a computationally efficient multiscale
pipeline designed to predict protein–drug association rate constants
and to provide mechanistic insights into the determinants of these rates,
such as conformational gating or induced fit. To apply this pipeline in an
automated and user-friendly manner, we developed SDAMD, a new software
tool integrated into the SDA7 software. SDAMD combines Brownian and
molecular dynamics simulations to exploit the advantages of both simulation
techniques while reducing their limitations. Brownian dynamics is used to
efficiently simulate the diffusional movement of molecules to compute the
diffusional association rates and to generate diffusional encounter complexes,
with optional evaluation of conformational selection effects when ensembles
of protein conformers are available. Subsequently, molecular dynamics refine
the generated encounter complexes by simulating internal motions and
induced-fit effects. SDAMD results have been validated for a set of proteins
and ligands that are diverse in their chemical nature, range of size and
flexibility. The predicted association rates show good agreement with experimentally
determined values, demonstrating the potential of this multiscale
framework to guide molecular design.