Localized Heuristic Inverse Quantitative Structure Activity Relationship with Bulk Descriptors Using Numerical Gradients

State-of-the-art
quantitative structure–activity relationship
(QSAR) models are often based on nonlinear machine learning algorithms,
which are difficult to interpret. From a pharmaceutical perspective,
QSARs are used to enhance the chemical design process. Ultimately,
they should not only provide a prediction but also contribute to a
mechanistic understanding and guide modifications to the chemical
structure, promoting compounds with desirable biological activity
profiles. Global ranking of descriptor importance and inverse QSAR
have been used for these purposes. This paper introduces localized
heuristic inverse QSAR, which provides an assessment of the relative
ability of the descriptors to influence the biological response in
an area localized around the predicted compound. The method is based
on numerical gradients with parameters optimized using data sets sampled
from analytical functions. The heuristic character of the method reduces
the computational requirements and makes it applicable not only to
fragment based methods but also to QSARs based on bulk descriptors.
The application of the method is illustrated on congeneric QSAR data
sets, and it is shown that the predicted influential descriptors can
be used to guide structural modifications that affect the biological
response in the desired direction. The method is implemented into
the AZOrange Open Source QSAR package. The current implementation
of localized heuristic inverse QSAR is a step toward a generally applicable
method for elucidating the structure activity relationship specifically
for a congeneric region of chemical space when using QSARs based on
bulk properties. Consequently, this method could contribute to accelerating
the chemical design process in pharmaceutical projects, as well as
provide information that could enhance the mechanistic understanding
for individual scaffolds.