Keywords: virtual screening, pharmacophore modeling, drug discovery, caveats
Computer-aided techniques, such as molecular modeling, docking, virtual screening, machine learning, are well approved for their usefulness in drug discovery, design and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings [1].
The number of small molecules from nature and their occupied chemical space is constantly increasing by the discovery of taxonomically diverse producing organisms and the diligent exploitation of already known material [2, 3].
Additionally, historical information from traditional medicine and findings from observational studies, and the increasing knowledge we observe in structural and biological data from new chemical entities, macromolecular targets and their physiological role in humans on the other hand provide an infinite source of data. Combining information derived from all these heterogeneous sources, structuring big data and not getting lost within it will be a future challenge in our society.
Strategies and examples will be presented on how to integrate chemoinformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines by streamlining experimental efforts. Awareness concerning data reliability, a critical view on and an unbiased attitude towards predicted results are indispensable prerequisites for successful projects. Considering their limits, pitfalls and exploiting their potential, computer-aided strategies will successfully guide future studies and thereby augment our knowledge on bioactive natural lead structures.
[1] Kaserer TK, Schuster D, Rollinger JM. Chemoinformatics in Natural Products Research. In: Applied Chemoinformatics. A Textbook; ed. Gasteiger, J.; Engel, T. 2nd ed., Wiley, 2018. doi: 10.1002/9783527806539.ch6c
[2] Pye CR, Bertin MJ, Lokey RS, Gerwick WH, Linnington RG ( 2017) Retrospective analysis of natural products provides insights for future discovery trends. PNAS USA 2017; 114: 5601-5606. doi: 10.1073/pnas.1614680114
[3] Boufridi A, Quinn RJ. Annu Rev PharmacolToxicol 2018; 58: 451-470. doi:10.1146/annurev-pharmtox-010716-105029