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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.

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.

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.

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.

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.