| Autor: |
Ancajas CMF; Department of Chemistry, Vanderbilt University, Nashville, TN, USA. allison.s.walker@vanderbilt.edu., Oyedele AS; Department of Chemistry, Vanderbilt University, Nashville, TN, USA. allison.s.walker@vanderbilt.edu., Butt CM; Department of Chemistry, Vanderbilt University, Nashville, TN, USA. allison.s.walker@vanderbilt.edu., Walker AS; Department of Chemistry, Vanderbilt University, Nashville, TN, USA. allison.s.walker@vanderbilt.edu.; Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA.; Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA. |
| Abstrakt: |
Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products. |
