Use of natural products as chemical library for drug discovery and network pharmacology

Abstract

Background: Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products.

Methodology: We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model.

Conclusions: Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins.

Figure 1. Distribution of five molecular descriptors of natural products and approved drugs.

Figure 2. The distribution in chemical space according to principal component analysis of natural products in UNPD and FDA-approved drugs.

The green triangles and black dots represent natural products and FDA-approved drugs, respectively.

Figure 3. Drug-target network of natural products and their experimental targets (DTNe).

The size of each node is proportional to its degree. The nodes are colored according to their shortest-path betweenness in the network. Circles and triangles correspond to small compounds (natural products or drugs) and target proteins, respectively.

Figure 4. Distribution of docking score of natural products.

Figure 5. Drug-target network of natural products and their computational targets.

Representations of the symbols are the same to Figure 3.

Figure 6. Prediction model of indications for natural products.