J Chem Inf Model - Knowledge-based libraries for predicting the geometric preferences of druglike molecules.

Tópicos

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Resumo

We describe the automated generation of libraries for predicting the geometric preferences of druglike molecules. The libraries contain distributions of molecular dimensions based on crystal structures in the Cambridge Structural Database (CSD). Searching of the libraries is performed in cascade fashion to identify the most relevant distributions in cases where precise structural features are poorly represented by existing crystal structures. The libraries are fully comprehensive for bond lengths, valence angles, and rotamers and produce templates for the large majority of unfused and fused rings. Geometry distributions for rotamers and rings take into account any atom chirality that may be present. Library validation has been performed on a set of druglike molecules whose structures were published after the latest CSD entry contributing to the libraries. Hence, the validation gives a true indication of prediction accuracy.

Resumo Limpo

describ autom generat librari predict geometr prefer druglik molecul librari contain distribut molecular dimens base crystal structur cambridg structur databas csd search librari perform cascad fashion identifi relev distribut case precis structur featur poor repres exist crystal structur librari fulli comprehens bond length valenc angl rotam produc templat larg major unfus fuse ring geometri distribut rotam ring take account atom chiral may present librari valid perform set druglik molecul whose structur publish latest csd entri contribut librari henc valid give true indic predict accuraci

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