<b>In Silico Studies as Support for Natural Products Research</b>

It can be argued that in silico studies do not receive enough attention despite being a key part of addressing the limitations of our laboratory facilities, the high cost of chemicals, and the equipment required for wet laboratory activities. Natural product studies are demanding higher costs of chemicals, reagents, and varied laboratory facilities. This becomes a serious limitation in getting data from natural product studies. In silico studies use chemical structures as inputs as well as software and online web servers to generate data to support, predict, and validate wet laboratory activities. Interaction studies use computational tools to calculate binding energies and other associated properties. Predictions are based on the structure-activity relationships derived from previously conducted preclinical and clinical studies. As a main component of in silico studies, the physicochemical and pharmacokinetic properties of small molecules can be determined using online web servers such as SwissADME and absorption, distribution, metabolism, excretion, and toxicity web servers. An interaction study uses molecular docking software such as AutoDock, AutoDock Vina, GOLD, and online servers such as SwissDock. Furthermore, the stabilities of complexes considered in interaction studies can be confirmed using molecular dynamics simulation software such as VMD. Prediction of activity spectra for substances (PASS) is widely used to predict biological activities for molecules based on multilevel neighborhoods of atom descriptors. In silico studies have played an important role in medicinal chemistry, pharmacology, and related research for screening, interaction studies, prediction, and other related purposes. Results of in silico predictions will not be far from wet lab activities as in most cases these studies consider previously attempted clinical and preclinical biological activities. Some examples are presented here to encourage the use of in silico studies.   Received: 15 July 2024 | Revised: 27 August 2024 | Accepted: 25 October 2024   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author.   Author Contribution Statement Fekade Beshah Tessema: Conceptualization, Methodology, Writing - original draft, Visualization. Tilahun Belayneh Asfaw: Validation, Writing - review & editing. Mesfin Getachew Tadesse: Resources, Visualization, Supervision. Yilma Hunde Gonfa: Validation, Writing - review & editing. Rakesh Kumar Bachheti: Resources, Visualization, Supervision.