Natural products have been used for disease treatment and healing since ancient times. They have obviously become an inspiration for modern medicinal chemistry, drug discovery and development. Approximately 40 % of the drugs approved by the FDA during the last decades were natural products, their derivatives, or synthetic mimetics related to natural products [1]. Nowadays, new drugs based on natural compounds are successfully applied to treat tumors, viral and bacterial diseases, and nervous disorders [2].
In response to the current drug discovery demand, Life Chemicals has created its proprietary collection of dedicated Screening Libraries of over 14,600 synthetic compounds similar to natural ones, using the following two approaches:
- Natural Product-like Compound Library by Similarity Search (11,400 compounds)
- Natural Product-like Compound Library by Chemoinformatics and Substructure Search (3,200 compounds)
These screening compound collections have already been recognized to be extremely useful tools for high throughput screening (HTS) and high content screening (HCS) programs.
The compound selection can be customized based on your requirements, cherry picking is available.
Please, contact us at orders@lifechemicals.com for any additional information and price quotations.
Background
As many as 20 % of natural products lay in the chemical space beyond the Lipinski’s “Rule of Five” (Ro5) and typically are discarded during the lead optimization process. Meanwhile, many of such natural product-like based drugs still show the potential ability to cure life-threatening diseases (for example, they are applied as HIV protease inhibitors, anticancer agents, and heart stimulators) [4-5]. Natural Products have a great deal of structural diversity. Compared to the typical synthetic small drug-like molecules, natural products tend to have more sp3-hybridized bridgehead atoms, more chiral centers, a higher oxygen content but lower nitrogen one, a higher molecular weight, a higher number of H-bond donors and acceptors, lower cLogP values and higher molecular rigidity, and preferably aliphatic rings over aromatic ones [6-8].
Remarkable structural diversity and drug-likeness of molecular scaffolds, identified in natural compounds, provide a basis for the design of novel natural product-derived compound libraries within attractive chemical space for drug discovery and lead optimization (Fig. 2) [9-10].
Figure 2. Small-molecule approved drugs 01JAN81 to 30SEP19; n = 1394 (Picture source: J. Nat. Prod. 2020, 83, 3, 770-803).
Natural Product-like Compound Library by Similarity Search
This Screening Library was designed by 2D fingerprint similarity filtering vs natural compound scaffolds. The commercial databases of Specnet, TARGETMOL, SELECKCHEM, ICC, AnalytiCon Discovery, TimTec, COCONUT were used as reference sets. A Tanimoto 85 % similarity cut-off was applied to result in about 11,400 structurally diverse compounds, available from the Life Chemicals HTS Compound Collection.
Figure 3. Drugs approved by the FDA in 2021 are classified based on chemical structure. Examples of our library compounds are similar to those two that were approved by the FDA this year [3].
Natural Product-like Compound Library by Chemoinformatics and Substructure Search
The Life Chemicals HTS Compound Collection was analyzed by two different methods, namely:
- Chemical descriptor calculation (7,300 compounds selected)
- Natural-likeness scoring (9,700 compounds selected)
By overlapping both small-molecule screening compound sets, over 3,200 natural product-like molecules with excellent characteristics were selected applying these two approaches [9, 11].
Chemical descriptor-based selection method
The selection has been made in two steps:
Substructure search for natural-like scaffolds and most relevant groups in the Life Chemicals HTS Compound Collection (Fig. 2) [9]:
| coumarins flavonoids aurones alkaloids (aloperine, cytisine, lupinine, colchicine) bile acids | aryl benzothiazole arylpiperazine arylpiperidine benzofuran benzoxazole benzodiazepine benzothiophene benzylpiperidine | indole indoline indolizine isoquinoline purine | quinazolinone quinoline quinoxaline steroide tetrahydroisoquinoline tetrahydroquinoline |
About 60,100 samples were selected from about 509,970 compounds.
Then, we carried out the method validation and calculated the parameters listed in Table 1 for Pure Natural Products (PNP, MNP), NPs and Derivatives/Analogs (SNP), NP-based Combinatorial Compounds (NatDiv), and LC Derivatives/Analogs (LC).
Figure 4. Compound distribution by the presence of the natural-like scaffolds in their chemical structure within the Natural Product-like Compound Library.
Natural-likeness scoring
The evaluation of the compound’s natural product-likeness is an important asset in the selection and optimization of natural product-like drugs and synthetic bioactive compounds. Natural-likeness scoring, based on the sum of the frequency of certain molecule fragments among natural products (NPs) and small molecules (SMs), was performed by the natural product-likeness calculator [13-14]. Its results, presented in Fig. 5 and Table 1, show the distribution of real natural products and natural-like compounds by the score.
Figure 5. Natural product-likeness scorer. Distribution of real natural products.
Table 1. Mean values of descriptors, which play the most important role in the characterization of natural products.
| PNP | MNP | SNP | NatDiv | LC | |
| MW | 393.9 | 503.6 | 409.2 | 441.3 | 389.2 |
| HAC | 28.2 | 34.6 | 29.1 | 31.1 | 27.7 |
| ClogP | 2.3 | 3.9 | 3.7 | 2.1 | 3.6 |
| H-donors | 2.7 | 2.6 | 1.4 | 2.3 | 1.4 |
| H-acceptors | 6.6 | 7.4 | 6.4 | 8.0 | 4.2 |
| TPSA | 98.9 | 108.9 | 83.2 | 104.7 | 79.8 |
| Ring count | 3.6 | 2.9 | 3.5 | 4.0 | 3.9 |
| Aromatic rings | 5.1 | 3.5 | 11.8 | 9.5 | 15.3 |
| Rotatable bonds | 5.2 | 11.5 | 6.1 | 5.3 | 5.0 |
| Number of N atoms | 0.7 | 1.2 | 2.1 | 3.6 | 2.6 |
| Number of O atoms | 5.9 | 6.1 | 4.3 | 4.4 | 3.1 |
| Number of chiral atoms | 5.5 | 6.3 | 1.4 | 2.3 | 1.3 |
| LipViol≥2 | 18% | 30% | 10% | 8% | 2% |
Representative compounds from Natural Product-like Compound Libraries
References
- D.J. Newman, G.M. Cragg. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J. Nat. Prod., 75 (2012), pp. 311-335
- Chopra B, Dhingra AK. Natural products: A lead for drug discovery and development. Phytother Res. 2021;35(9):4660-4702. doi:10.1002/ptr.7099
- de la Torre BG, Albericio F. The Pharmaceutical Industry in 2021. An Analysis of FDA Drug Approvals from the Perspective of Molecules. Molecules. 2022;27(3):1075. Published 2022 Feb 5. doi:10.3390/molecules27031075
- Quinn, R. J. et al. Developing a drug-like natural product library. J. Nat. Prod. 71, 464–468 (2008).
- Doak, B. C., Over, B., Giordanetto, F. & Kihlberg, J. Oral druggable space beyond the rule of 5: Insights from drugs and clinical candidates. Chemistry and Biology 21, 1115–1142 (2014).
- Rodrigues, T., Reker, D., Schneider, P. & Schneider, G. Counting on natural products for drug design. Nat. Chem. 8, 531–541 (2016).
- Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).
- Atanasov AG, Zotchev SB, Dirsch VM; International Natural Product Sciences Taskforce, Supuran CT. Natural products in drug discovery: advances and opportunities. Nat Rev Drug Discov. 2021;20(3):200-216. doi:10.1038/s41573-020-00114-z
- Properties and Architecture of Drugs and Natural Products Revisited. Kristina Grabowski and Gisbert Schneider.
- Matthew E Welsch, Scott A Snyder, and Brent R Stockwell, Current Opinion in Chemical Biology 2010,141–15
- Atanasov AG, Zotchev SB, Dirsch VM; International Natural Product Sciences Taskforce, Supuran CT. Natural products in drug discovery: advances and opportunities. Nat Rev Drug Discov. 2021;20(3):200-216. doi:10.1038/s41573-020-00114-z
- Scaffold diversity of natural products: inspiration for combinatorial library design. Kristina Grabowski,a Karl-Heinz Baringhausb and Gisbert Schneider
- Natural Product-likeness Score and Its Application for Prioritization of Compound Libraries. Peter Ertl, Silvio Roggo, and Ansgar Schuffenhauer. J. Chem. Inf. Model. 2008, 48, 68-74
- Vanii Jayaseelan, K., Moreno, P., Truszkowski, A. et al. Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinformatics 13, 106 (2012).