Versatile generation of accurate 3D molecular models
The Advantages of Accurate 3D Ligand Libraries
Computational methods have become an indispensable part of lead identification efforts. Nearly all methods require accurate 3D molecular models as a starting point. However, many corporate and purchasable compound databases contain only 2D molecular structures. Efficient and accurate 2D to 3D conversion is therefore a key precursor to computational analyses.
Beyond simple one-to-one structural conversion, it is equally important to generate scientifically sound molecular models that enumerate the different structural and chemical possibilities a ligand could sample, as these variations could lead to dramatically different results in subsequent computations. A versatile conversion program that can be configured to generate ligand libraries with the desired structural and chemical features can significantly streamline the entire in silico drug discovery process.
Chemically correct models:
LigPrep generates accurate, energy minimized 3D molecular structures. LigPrep also applies sophisticated rules to correct Lewis structures and to eliminate mistakes in ligands in order to reduce downstream computational errors.
LigPrep optionally expands tautomeric and ionization states, ring conformations, and stereoisomers to produce broad chemical and structural diversity from a single input structure.
LigPrep applies filters to eliminate compounds that do not meet user-specified criteria, allowing the generation of a completely customized ligand library.
Preprocessing for Schrödinger simulations:
LigPrep has settings specially tuned for generating input structures for Glide and Phase. Using these settings will optimize the output structures to meet the requirements of the simulation programs without necessitating any further user intervention.
LigPrep processes approximately one ligand per second, making it possible to convert entire databases at one time.
Citations and Acknowledgements
Schrödinger Release 2017-1: LigPrep, Schrödinger, LLC, New York, NY, 2017.