Version 1
: Received: 21 November 2018 / Approved: 23 November 2018 / Online: 23 November 2018 (13:56:32 CET)
How to cite:
Ntie-Kang, F.; Nyongbela, K.D.; Ayimele, G.A.; Shekfeh, S. “Drug-Likeness” versus “Natural Product-Likeness”. Preprints2018, 2018110561. https://doi.org/10.20944/preprints201811.0561.v1.
Ntie-Kang, F.; Nyongbela, K.D.; Ayimele, G.A.; Shekfeh, S. “Drug-Likeness” versus “Natural Product-Likeness”. Preprints 2018, 2018110561. https://doi.org/10.20944/preprints201811.0561.v1.
Cite as:
Ntie-Kang, F.; Nyongbela, K.D.; Ayimele, G.A.; Shekfeh, S. “Drug-Likeness” versus “Natural Product-Likeness”. Preprints2018, 2018110561. https://doi.org/10.20944/preprints201811.0561.v1.
Ntie-Kang, F.; Nyongbela, K.D.; Ayimele, G.A.; Shekfeh, S. “Drug-Likeness” versus “Natural Product-Likeness”. Preprints 2018, 2018110561. https://doi.org/10.20944/preprints201811.0561.v1.
Abstract
We discuss further details on the concepts of “drug-likeness”, “lead-likeness”, and “natural product-likeness”. The discussion will first focus on natural products as drugs, then a discussion of previous studies in which the complexities of the scaffolds and chemical space of naturally occurring compounds have been compared with synthetic, semi-synthetic compounds and FDA-approved drugs. This is followed by guiding principles for designing “drug-like” natural product libraries for lead compound discovery purposes. We end up by presenting a tool for measuring “natural product-likeness” of compounds and a brief presentation of machine learning approaches and a binary quantitative structure-activity relationship (QSAR) for classifying drugs from non-drugs and natural compounds from non-natural ones, respectively.
Keywords
cheminformatics, drugs, drug-likeness, drug discovery, natural products
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
