3D-QSAR refers to the application of force field calculations, requiring the three-dimensional structure of a given set of small molecules (training set) with known activities. The training set needs to be superimposed (aligned) by experimental data (based on ligand-protein crystallography) or molecular superposition software. It uses calculated potentials, such as Lennard-Jones potentials rather than experimental constants, but related to the entire molecule, rather than individual substituents. It studies the spatial field (the shape of the molecule) and the electrostatic field related to each other through partial least squares regression (PLS).
Molecule mining approaches is a special case of structured data mining approaches. It applies a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures. There are also other approaches using maximum common subgraph searches or graph kernels.
Typical QSAR models derived from nonlinear machine learning are seen as a "black box," which fails to guide medicinal chemists. There is a relatively new concept of matched molecular pair analysis or prediction driven MMPA that is coupled with QSAR model to identify activity cliffs.
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