| Abstract: |
The key to applying machine inference to scientific
problems is to have an appropriate representation of the problem. The most
successful applications of inductive logic programming (ILP) have been in the
field of learning molecular quantitative structure activity relationships
(QSARs). The basic representation used in these applications has been based on
atoms linked together by bonds. This representation, although successful and
intuitive, ignores the most basic discovery of 20th century chemistry, that
molecules are quantum mechanical objects. Here we present a new representation
using quantum topology (StruQT) for machine inference in chemistry which is
firmly based on quantum mechanics. The new representation is based on Richard
F.W. Bader's quantum topological atoms in molecules (AIM) theory. Central to
this representation is the use of critical points in the electron density
distribution of the molecule. Other gradient fields such as the Laplacian of the
electron density can also be used. We have demonstrated the utility of the use
of AIM theory (in a propositional form) in the regression problem of predicting
the lowest UV transition for a system of 18 anthocyanidans. The critical point
representation of fields can be easily mapped to a logic programming
representation, and is therefore well suited for ILP. We have developed an
ILP-StruQT method and are currently evaluating it for QSAR problems.
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