This volume highlights some of the advances in molecular similarity. Molecular similarity research is a dynamic field where the rapid transfer of ideas and methodologies from the theoretical, quantum chemical and mathematical chemistry disciplines to efficient algorithms and computer programs used in industrially important applications is especially evident. These applications often serve as motivating factors toward new advances in the fundamental and theoretical fields, and the combination of intellectual challenge and practical utility provides mutual advantages to theoreticians and experimentalists. The aim of this volume is to present an overview of the current methodologies of molecular similarity studies, and to point out new challenges, unsolved problems, and areas where important new advances can be expected.
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978-0-08-055226-2 (9780080552262)
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Schweitzer Klassifikation
Quantum similarity. Fuzzy sets and boolean tagged sets; vector semispaces and convex sets; quantum similarity measures and ASA density functions; diagonal vector spaces and quantum chemistry. Pattern recognition techniques in molecular similarity. Topology and the quantum chemical shape concept. Structural similarity analysis based on topological fragment spectra. Analysis of the transferability of similarity calculations from substructures to complex compounds. Similarity in organic synthesis design: comparing the syntheses of different compounds. Browsable structure-activity datasets. Characterization of the molecular similarity of chemicals using topological invariants. Optimizing hybrid density functionals by means of quantum molecular similarity techniques. Atomic similarity through a neural network: self-associative periodic table of elements. Comparison of quantum similarity measures derived from one-electron, intracule and extracule densities. The complementarity principle and its uses in molecular similarity and related aspects. Correlations and applications of the circumscribing/excised internal structure concept. Least-squares and neural-network forecasting from critical data: diatomic molecular re and triatomic IP and DHa.
