Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Abstract

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

An Overview of COVID-19 and the Potential Plant Harboured Secondary Metabolites against SARS-CoV-2: A Review

“…Bioactive compounds are constantly being investigated in creating novel bioactive molecules with industrial uses, gaining attention from the scientific community due to their pharmacophorelike structures, pharmacokinetic qualities, and chemical space 42 . Computational approaches have revolutionised the identification of novel specific bioactive compounds by evaluating vast structural libraries in silico against a bioreceptor or biological system, lowering the cost, facilities, and time to identify new compounds 42,43 . Machine learning-based models trained on specific proteins have provided affordable and quick-to-implement approaches for the development of successful viral treatments in the recent decade.…”

“…Library preparation consists of structure of compounds obtaining and convert them into readable formats like SMILES, SDF and MOL2 (simplified molecular-input line entry system, structure data file and MDL Molfile). The computational techniques includes both 42 . The majority of machine learning drug discovery studies of SARS-CoV-2 focused on viral protiens such as spike (S) protein, MPro, 3CLPro etc., protein ligand interaction, binding effeciency, and binding free energy are also considered in the process 44,49,50 .…”

“…Later by adapting the deep docking, QSAR etc., are using to identify the protein inhibitors from available databases like FDA approved drugs, ZINC database etc. In majority of studies 3CLPro of SARS-CoV-2 is target protein, Deep learning model and molecular dynamic simulation methods approched to finilise the highest hit compound to target protein like 3CLPro of SARS-CoV-2 42,44,[49][50][51][52][53] . Some of the extracts of litchi seeds, Houttuynia cordata, Chinese Rhubarb extracts, Isatis indigotica plants (beta-sistosterol) root extract can inhibit the SARS enzymatic activity 54 .…”

Myostatin and its Regulation: A Comprehensive Review of Myostatin Inhibiting Strategies

“…Undoubtedly, there are limitations of the VS approach that should be taken into account. The prevalence of stereochemical and valence mistakes in biochemical compound libraries may potentially result in inviable molecules ( Williams et al, 2012 ; Santana et al, 2021 ). There are a variety of open source and licensed virtual screening software/tools available, and each has its own constraints that must be overcome to prevent the production of erroneous conclusions or artifacts ( Gimeno et al, 2019 ).…”

Multi-Omics Data Mining: A Novel Tool for BioBrick Design

“…NPs have been used for ages as medicines than the synthetic bioactives and as scaffolds for the rational design of novel synthetic drugs [136,137]. Interestingly, they occupy a much larger fraction of the ensemble of all chemical compounds (i.e., have a larger structural diversity), which is classically known among theoretical and computational chemists as chemical space ($10 60 molecules) [138,139]. In the field of medicinal chemistry, and considering we only know just a bit portion of the estimated chemical space ($10 8 molecules) [140], the use of NP-based libraries represents a priceless opportunity for scientists to make bigger and faster leaps within it [141,142].…”