Title:Molecular Modeling Techniques Applied to the Design of Multitarget
Drugs: Methods and Applications
Volume: 22
Issue: 5
Author(s): Larissa Henriques Evangelista Castro and Carlos Mauricio R. Sant'Anna*
Affiliation:
- Programa de Pós-Graduação em Química, Instituto de Química, Universidade Federal Rural do Rio de Janeiro,
Seropédica, Brasil
- Departamento de Química Fundamental, Instituto de Química, Universidade Federal Rural do
Rio de Janeiro, Seropédica, Brasil
Keywords:
Multitarget Drug design, SDBB, LBDD, Molecular hybridization, Artificial intelligence, Machine learning.
Abstract: Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional
“one-target, one disease” paradigm due to their complex pathogenic mechanisms. Although a
combination of drugs can be used, a multitarget drug may be a better choice due to its efficacy, lower
adverse effects and lower chance of resistance development. The computer-based design of these
multitarget drugs can explore the same techniques used for single-target drug design, but the
difficulties associated with the obtention of drugs that are capable of modulating two or more targets
with similar efficacy impose new challenges, whose solutions involve the adaptation of known
techniques and also to the development of new ones, including machine-learning approaches. In
this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together
with some cases where the application of such techniques led to effective multitarget ligands.