Drug design and discovery is a process that requires high financial cost, as time-consuming. For many years, this process was focused on empirical pharmacology. However, over the years, the approach target-based allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, the rational drug design benefited by increasing computer engineering and software development, computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach was able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach showed their importance, being CADD essential in any drug discovery campaign. Thus, this perspective paper will show the prominent success cases in this approach and the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.
This provides a better insight into the mode of interaction of various cruzain inhibitors, which show IC50 values in the nanomolar range but which do not interact with the triad. These findings can help researchers to find new cruzain inhibitors for use in the fight against the Chagas disease.
The results reported in this study will advance our understanding of the influence of the distinct chemical structures of inhibitors at the active site and aid the development of new virtual screening protocols to design novel AChE multi-target inhibitors.
Background:
Computer-Aided Drug Design (CADD) techniques have garnered a great deal of attention in academia and industry because of their great versatility, low costs, possibilities of cost reduction in in vitro screening and in the
development of synthetic steps; these techniques are compared with high-throughput screening, in particular for candidate
drugs. The secondary metabolism of plants and other organisms provide substantial amounts of new chemical structures,
many of which have numerous biological and pharmacological properties for virtually every existing disease, including cancer. In oncology, compounds such as vimblastine, vincristine, taxol, podophyllotoxin, captothecin and cytarabine are examples of how important natural products enhance the cancer-fighting therapeutic arsenal.
Objective:
In this context, this review presents an update of Ligand-Based Drug Design and Structure-Based Drug Design
techniques applied to flavonoids, alkaloids and coumarins in the search of new compounds or fragments that can be used in
oncology.
Methods:
A systematical search using various databases was performed. The search was limited to articles published in the
last 10 years.
Conclusion:
The great diversity of chemical structures (coumarin, flavonoids and alkaloids) with cancer properties, associated with infinite synthetic possibilities for obtaining analogous compounds, creates a huge chemical environment with potential to be explored, and creates a major difficulty, for screening studies to select compounds with more promising activity
for a selected target. CADD techniques appear to be the least expensive and most efficient alternatives to perform virtual
screening studies, aiming to selected compounds with better activity profiles and better “drugability”.
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