1996/01/01 by Jan Schuur, Paul M. Selzer, Johann Gasteiger · 1 voice · 5 citations
Chemistry · Computer Science · Materials Science · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Molecular spectroscopy and chirality
paper · doi:10.1021/ci950164c
openalex publication_date 1996/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
A molecular transform, derived from an equation used in electron diffraction studies, is developed that allows the representation of the three-dimensional structure of a molecule by a fixed number of values. Various atomic properties can be taken into account giving high flexibility to this representation of a molecule. This 3D-MoRSE ( Mo lecule R epresentation of S tructures based on E lectron diffraction) code retains important structural features such as the mass (see ref 35) and the amount of branching as evidenced by an investigation of monosubstituted benzene derivatives. Furthermore, this molecular representation was able to distinguish between benzene, cyclohexane, and naphthalene derivatives in a dataset of great structural variety. This molecular representation was used in counterpropagation neural networks to distinguish between dopamine D1 and D2 agonists and to group 31 steroids binding to the corticosteroid binding globulin receptor into compounds of high, medium, and low activity. Great promise is given to this representation of molecular structures for the simulation of infrared spectra as revealed by an investigation of monosubstituted benzene derivatives.