<p>Accurate
predictions of changes to protein-ligand binding affinity in response to
chemical modifications are of utility in small molecule lead optimization.
Relative free energy perturbation (FEP) approaches are one of the most widely
utilized for this goal, but involve significant computational cost, thus
limiting their application to small sets of compounds. Lambda dynamics, also
rigorously based on the principles of statistical mechanics, provides a more
efficient alternative. In this paper, we describe the development of a workflow
to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run
on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The
workflow establishes a framework for setting up simulation systems for
exploratory screening of modifications to a lead compound, enabling the
calculation of relative binding affinities of combinatorial libraries. To
validate the workflow, a diverse dataset of congeneric ligands for seven
proteins with experimental binding affinity data is examined. A protocol to
automatically tailor fit biasing potentials iteratively to flatten the free
energy landscape of any MSLD system is developed that enhances sampling and
allows for efficient estimation of free energy differences. The protocol is
first validated on a large number of ligand subsets that model diverse
substituents, which shows accurate and reliable performance. The scalability of
the workflow is also tested to screen more than a hundred ligands modeled in a
single system, which also resulted in accurate predictions. With a cumulative
sampling time of 150ns or less, the method results in average unsigned errors of
under 1 kcal/mol in most cases for both small and large combinatorial libraries.
For the multi-site systems examined, the method is estimated to be more than an
order of magnitude more efficient than contemporary FEP applications. The
results thus demonstrate the utility of the presented MSLD workflow to
efficiently screen combinatorial libraries and explore chemical space around a
lead compound, and thus are of utility in lead optimization.</p>
Efficient Estimation of the ANOVA Mean Dimension, with an Application to Neural Net Classification
