Natural products have been widely used in disease treatment and have become an inspiration for modern drug discovery and development. Screening of natural products has promoted the discovery of a large number of drugs, and natural product-like fragment has an important position in drug development as promising starting points within attractive chemicals space. Scientists enrich small libraries with compounds possessing structural motifs validated by nature allows reaching better molecular profile and improves biological response. In the past few decades, about 40% of the marketed drugs are natural products, their derivatives or synthetic mimetics related to natural products.
At BOC Sciences, remarkable structural diversity and drug-likeness of molecular scaffolds, identified in natural compounds are applied as a basis for the design of new natural product-derived compound libraries for drug discovery. The implementation of natural product-like frameworks into fragment-based drug discovery benefit from the use of biologically validated areas of chemical space not occupied by average synthetic molecules.
Figure 1. Similarity measures for interaction fingerprints. (Rácz, A.; et al. 2018)
Synthesis Methods
Our Natural Product-like Fragment Library contains more than 7,500 synthetic compounds similar to natural compounds. BOC Sciences has developed the following two methods to design and synthesize our proprietary Natural Product-like Fragment Library:
Similarity search-based method
- We use 2D fingerprint similarity filtering and natural composite scaffolds to design natural product-like compound libraries based on similarity search
- Commercial databases such as Specnet, TARGETMOL, SELECKCHEM, ICC, AnalytiCon Discovery, and TimTec are available as reference sets
- Tanimoto's 85% similarity cutoff is applied to generate approximately 4,400 structurally different compounds, available from our HTS Compound Collection.
Table 1. Substructure search for natural-like scaffolds and most relevant groups in our HTS Compound Collection.
| natural-like scaffolds | relevant groups |
| coumarins, urolithins, flavonoids, aurones, alkaloids (aloperine, cytisine, lupinine, colchicine), bile acids | aryl benzothiazole, benzofuran, benzoxazole, carbohydrate, arylpiperazine, arylpiperidine, benzodiazepine, benzothiophene, benzylpiperidine, indole, indoline, indolizine, isoquinoline, purine, quinazolinone, quinoline, quinoxaline,steroide, tetrahydroisoquinoline, chromone, tetrahydroquinoline |
Chemoinformatics and Substructure Search-based method
Two different methods - chemical descriptor calculation and natural similarity score are used to analyze the HTS compound library to obtain the natural product-like compound library. By overlapping the two sets of analysis results, about 3,300 stock small-molecule screening compounds with excellent characteristics are obtained. At BOC Sciences, a large number of compounds can be selected using these two methods (chemical descriptor calculation and natural-likeness scoring).
- Descriptor-based selection method
The selection is carried out in two steps:
1. Search for substructures of natural scaffolds and most relevant groups in the HTS Compound Collection
2. Verify the method and calculate the parameters listed in the following Table 2
- Natural-likeness scoring method
We use the Natural product-likeness calculator to perform Natural-likeness scoring which shows the distribution of real natural products and product-like compounds
Table 2. Mean values of descriptors, which play the most important role in the characterization of natural products.
| Pure Natural Products | Derivatives/Analogs | NP-based Combinatorial Compounds | LC Derivatives/Analogs | |
| MW | 394 | 410 | 441 | 390 |
| Number of Rotatable Bonds | 5 | 11 | 5 | 5 |
| Number of H Donors | 3 | 1.4 | 2.3 | 1.4 |
| Number of H Acceptors | 6.6 | 7.4 | 8.0 | 4.2 |
| TPSA | 98.9 | 83.2 | 104.7 | 79.8 |
| Number of Ring | 3.6 | 3.5 | 4.0 | 3.9 |
| Number of N Atoms | 0.7 | 2.1 | 3.6 | 2.6 |
| Number of O Atoms | 5.9 | 4.3 | 4.4 | 3.1 |
| Number of Chiral Atoms | 5.5 | 1.4 | 2.3 | 1.3 |
| CLogP | 2.3 | 3.7 | 2.1 | 3.6 |
| LipViol≥2 | 18% | 10% | 8% | 2% |
| HAC | 28.2 | 29.1 | 31.1 | 27.7 |
Features of Natural Product-like Fragment Library
- A wide range of chemical structure dissimilarity and are compliant with in-house PAINS structural filters
- All compounds are filtered according to the the well-known Rule of Three criteria
- Scaffold tree analysis is used to extract multiple level-two and level-three scaffolds from our General Natural Product Database
- After excluding undesirable and primitive chemotypes, the remaining structures are used for sub-structure search within the General Fragment Collection
- Our fragments derived from the scaffold have at least 85% similarity with a set of scaffolds derived from natural products
- Perform additional structural filtering
- All compounds can be used for cherry-picking
What We Deliver
- Delivered within 2 weeks in any customer-preferred format
- Powders, dry films or DMSO solutions formatted in vials, 96 or 384-well plates
- All compounds have a minimum purity of 90% assessed by 1H NMR
- Analytical data is provided
BOC Sciences provides professional, rapid and high-quality services of Natural Product-like Fragment Library design at competitive prices for global customers. Personalized and customized services of Natural Product-like Fragment Library design can satisfy any innovative scientific study demands. Our clients have direct access to our staff and prompt feedback to their inquiries. If you are interested in our services, please contact us immediately!
Reference
- Rácz, A.; et al. Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints. Journal of Cheminformatics. 2018. 10.
