Abstract
ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements (“flips” of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation.
Highlights
ForceGen is a method for 3D structure generation and conformational elaboration that does not rely on distance geometry [1,2,3,4,5,6], precalculated molecular templates [7, 8], or stochastic sampling [9,10,11]
ForceGen was introduced with analysis of both 3D structure generation and conformer generation using five data sets, but comparison to other approaches was limited by the size of available benchmarking data sets and the breadth of available comparative data to recent versions of widely used methods [18]
The ForceGen Set will be used to characterize performance gains attributable to the new search strategies, and the other three macrocycle sets will be used for comparison to other methods
Summary
ForceGen is a method for 3D structure generation and conformational elaboration that does not rely on distance geometry [1,2,3,4,5,6], precalculated molecular templates [7, 8], or stochastic sampling [9,10,11]. The original ForceGen report showed comparative performance using 30 macrocyclic ligands from the widely used Chen and Foloppe benchmark [20], but more substantial analysis was presented on a set of 182 macrocycles curated from the PDB, which will be used here to compare current and prior ForceGen performance. The data include 431 unique macrocyclic ligands, forming the largest such set analyzed in a single study
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