  We present an approach to combine novel molecular features with experimental
data within a data-driven pipeline. The method is applied to the challenge of
predicting the reactivity of a series of sulfonyl fluoride molecular fragments
used for drug discovery of targeted covalent inhibitors. We demonstrate utility
in predicting reactivity using features extracted from a workflow which employs
quantum embedding of the reactive warhead using density matrix embedding
theory, followed by Hamiltonian simulation of the resulting fragment model from
an initial reference state. These predictions are found to improve when
studying both larger active spaces and longer evolution times. The calculated
features form a `quantum fingerprint' which allows molecules to be clustered
with regard to warhead properties. We identify that the quantum fingerprint is
well suited to scalable calculation on future quantum computing hardware, and
explore approaches to capture results on current quantum hardware using error
mitigation and suppression techniques. We further discuss how this general
framework may be applied to a wider range of challenges where the potential for
future quantum utility exists.
