Abstract
Traditionally, the first step in the development of drugs is the definition of the
target, by choice of a biological structure involved in a disease or by recognition
of a molecule with some degree of a biological activity that presents itself as druggable
and endowed with therapeutic potential. The complexity of the pathophysiological mechanisms
of disease and of the structures of the molecules involved creates several challenges
in this drug discovery process. These difficulties also come from independent operation
of the different parts involved in drug development, with little interaction between
clinical practitioners, academic institutions and large pharmaceutical companies.
Research in this area is purpose specific, performed by specialized researchers in
each field, without major inputs from clinical practitioners on the relevance of such
strategy for future therapies. Translational research can shift the way these relationships
operate towards a process in which new therapies can be generated by linking experimental
discoveries directly to unmet clinical needs. Computational chemistry methods provide
valuable insights on experimental findings and pharmacological and pathophysiological
mechanisms, allow the virtual construction of new possibilities for the synthesis
of new molecular entities, and pave the way for informed cost-effective decisions
on expensive research projects. This text focus on the current computational methods
used in drug design, how they can be used in a translational research model that starts
from clinical practice and research-based theorization by medical practitioners and
moves to applied research in a computational chemistry setting, aiming the development
of new drugs for clinical use.
Key words
drug design - computational chemistry - clinical expertise - translational research
