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

• Published in: Frontiers in chemistry Vol. 9; p. 662688
• Main Authors: Santana, Kauê, do Nascimento, Lidiane Diniz, Lima E Lima, Anderson, Damasceno, Vinícius, Nahum, Claudio, Braga, Rodolpho C, Lameira, Jerônimo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 29.04.2021
• Subjects: big data; bioprospecting; cheminformatics; Chemistry; machine learning; natural products; virtual screening; bioprospecting; natural products; drug discovery; virtual screening; big data; machine learning; chemical data; cheminformatics
• ISSN: 2296-2646; 2296-2646
• DOI: 10.3389/fchem.2021.662688

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 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.