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Pharmacognosy in  Modern Drug Discovery           

Written by Mehul Bhatt

Published on: October 22, 2026in Issue 10 - October 2026
Explore the role of natural products in drug discovery and how they contribute to chemical and biological diversity.
Natural Products in Drug Discovery
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Last Updated: October 22, 2026

Estimated reading time: 8 minutes

INTRODUCTION

Pharmacognosy has evolved from a descriptive science of crude natural products to an integrative discovery discipline that combines ethnobotany, natural product chemistry, pharmacology, genomics, metabolomics, synthetic biology, and computation. However, The main benefit is still chemical and biological diversity. Natural products explore structural space shaped by evolutionary interactions with biological macromolecules and are therefore useful both as drug leads and as probes of cell biology (Yao & Wei, 2018). The question is therefore not whether natural products are “old-fashioned”, but how to make their discovery more selective, reproducible, sustainable and compatible with contemporary development constraints.

1. SOURCES OF PHARMACOGNOSTIC DISCOVERY

Traditional medicines and plants

Although plants are still a important source of bioactive compounds, modern methods tend to separate out the three activities. Which have always been combine in the past: recording the traditional use, identifying the active constituents. And assessing whether an isolated molecule still possesses the activity of the original preparation. Thomford et al. (2018). examined plant-derived products and point out that traditional medicines are generally used in the form of extracts or mixtures, while modern drug development usually aims at obtaining purify active compounds. They also warn that isolating a single ‘active’ component can at times lead to a reduction or complete elimination of the activity seen in the original preparation. Therefore, standardization, mixture pharmacology, exposure, and reproducibility must lie at the heart of pharmacognosy, not just on the periphery.

The microbiota of terrestrial organisms

Bacteria, fungi, endophytes, symbionts, as well as extremophiles possess biosynthetic mechanisms by which antibiotics, immunomodulators, antitumor agents, and other specialize metabolites can be produce. Nowadays, genomics and metagenomics have made it possible to identify biosynthetic gene clusters that could not have been reach through culture-based isolation alone, and synthetic biology together with genome engineering can assist in expressing and optimising these products in either native or heterologous hosts (Zhang et al., 2017).

The variety of marine organisms and their venoms has expanded the range of available chemicals beyond what can be obtain from land sources. Past reviews have listed marine chemotypes and have identifies ziconotide, which comes from the venom of Conus magus, as a marine-derived drug that has been approved for use in the treatment of chronic pain. Moreover, other marine-derived agents with anticancer properties have also reached clinical development (Cragg & Newman, 2014). Venoms and other products derived from animals are especially useful when peptides or molecules that interact with proteins are need. Although problems relating to delivery, immunogenicity, stability, and manufacturing can take over the development process.

2.TECHNOLOGIES CHANGING THE WORKFLOW

Dereplication and metabolomics

The traditional method—comprising the steps of extraction, fractionation, isolation, identification, and testing—frequently led to the re-discovery of already known compounds and involved a great deal of work on substances that were inactive or not well characterised. However, By using LC–high-resolution mass spectrometry and NMR-based dereplication. Chemical profiles can be determined early on, known metabolites can be identifies. Unusual features can be given priority and a link between chemistry and bioactivity can be establishes (Tawfike et al., 2013). Metabolomics also aids in process development since the datasets can be used to guide the conditions for cultivation and fermentation. In such a way that the production of the desires metabolite is kept up or increases when the process is scaled up (Tawfike et al., 2013).

Genomics, genome mining, and molecular networking

Researchers are able to work out a organism’s ability to produce compounds before they isolate the compound. By using integrated metabolomic–genomic workflows it is possible to prioritise strains, cut down on rediscovery, link gene clusters to chemical structures, and make use of comparative evolution, retrobiosynthetic prediction, molecular networking, and computational analysis in order to speed up the discovery process (Avalon et al., 2022). The problem now is not merely the identification of gene clusters. But rather connecting the predict clusters to the chemistry that is expresses, to biological function, to scalable production and to a candidate that is feasible for development.

Chemical biology and phenotypic screening: Natural Products in Drug Discovery

Phenotypic discovery is particularly appropriate for natural products since the complex, stereochemically detail. And at times covalent or allosteric nature of their chemotypes can reveal biological targets that are misses by target-first screening methods. Natural-product-inspired libraries go beyond simply using molecules that are directly produces by nature; by means of biology-oriented synthesis and similar strategies, new areas of chemical space not accessed by biosynthesis are explore, and phenotypic screens can be combines with target-identification techniques in order to progress from cellular activity to a understanding of the mechanism (Laraia & Waldmann, 2017). The benefit is not just the identification of a lead compound but also the acquisition of a new biological tool or the discovery of a mechanism relevant to disease.

Synthetic biology, semi-synthesis, and total synthesis

Chemical synthesis is more than replicating a scarce natural molecule. It can provide material, reduce a scaffold, increase potency or selectivity, change pharmacokinetics, and produce analogs to probe structure-activity relationships. Biosynthetic engineering complements this by modifying pathways or expressing gene clusters in production hosts. Together, these approaches address the classical problems of low titers, difficult isolation. However, limited supply that contributed to the contraction of industrial natural-product programs in the 1990s(Zhang et al., 2017).

Artificial intelligence and data integration

Machine learning can support structure annotation, biological-activity prediction, molecular generation, prioritization of genome mining, and integration of chemical, genomic, phenotypic, as well as clinical data. A recent review frames the opportunity as a synergy between computational omics. Which exposes hidden natural-product diversity—and AI methods that predict activity and assist de novo design(Mullowney et al., 2023). Limiting factors include the need for high-quality datasets and appropriate validation strategies before discovery workflows can rely on these methods (Mullowney et al., 2023).

3. WHAT PHARMACOGNOSY CONTRIBUTES TO MODERN DISCOVERY

Fig.1 Pharmacognosy in  Modern Drug Discovery           
Fig.1 Pharmacognosy in  Modern Drug Discovery           

The strongest contribution is not a single source category or technology. It is the combination of evolutionary chemical diversity, phenotypic biological information, and route-to-supply options. Natural products have historically yielded medicines across anti-infective, cardiovascular, pain, antiparasitic, and oncology areas. The historical record includes morphine, quinine, penicillin-class antibiotics, statin models, plant-derived anticancer agents, and marine-derived therapeutics. (Cragg & Newman, 2014). Modern platforms make this legacy more actionable by reducing rediscovery, linking genotype to chemistry, and enabling analog design.

But the development case depends on more than activity. Candidate selection must consider efficacy, safety, pharmacokinetics, and manufacturability. Modern technologies like AI, organ-on-chip platforms, microfluidics, and computational design are propose to evaluate these aspects earlier and more efficiently. (Thomford et al., 2018). Discovery signals are a potent extract or novel metabolite, not yet a drug candidate.

4. FUTURE DIRECTION: NATURAL PRODUCTS IN DRUG DISCOVERY

  1. The selection of samples for the purpose of biodiversity research can increasingly make use of genomic data on biosynthetic potential before carrying out extensive isolation of compounds. (Avalon et al., 2022; Zhang et al., 2017).
  2. The discovery process goes from the compound through to the linked mechanism. Phenotypic screening can be combine with the most advanced target identification methods in order to link cellular activity to mechanism. (Laraia and Waldmann, 2017).
  3. Instead of relying on scarce natural resources we can use genome engineering, heterologous expression and fermentation optimisation. When native production is a limiting factor, these methods provide possibilities for producing pharmaceutically relevant metabolites and derivatives. (Tawfike et al., 2013; Zhang et al., 2017).
  4. AI will be most useful when data from separate data sets are combine into interoperable evidence systems, such as validated knowledge systems containing chemical structures, spectra, gene clusters, phenotypes, assay metadata, exposure data and clinical observations. The main constraint facing the field is probably the quality of the data and the need for prospective validation rather than the absence of modelling methods (Mullowney et al., 2023).
  5. From extraction to stewardship Sustainable sourcing, benefit-sharing, traceability and respect for traditional knowledge must all be built into the process. In its reviews of pharmacognosy, the issue of sustainable sourcing has been identified as an ongoing challenge and a future priority. (Mangla & Jahan, 2019).

CONCLUSION: NATURAL PRODUCTS IN DRUG DISCOVERY

Modern pharmacognosy is best view as a platform for discovery and explanation of biologically active chemistry, rather than as a catalog of crude drugs. The future is to link traditional and ecological knowledge to metabolomics, genome mining, phenotypic screening, synthetic biology, chemical biology and AI. To make the resulting candidates reproducible, scalable, mechanistically understood and ethically sourced (Avalon et al., 2022; Mangla & Jahan, 2019; Mullowney et al., 2023; Thomford et al., 2018). Ultimately, the field will be judged on whether these integrations increase the number of structures found or the fraction of natural product hits that survive pharmacology, safety, manufacturing, and clinical translation.

References:

  1. Yao Z, Wei W (2018) Natural Product Synthesis for Drug Discovery and Chemical Biology. Efficiency in Natural Product Total Synthesis. https://doi.org/10.1002/9781118940228.CH10
  2. Thomford NE, Senthebane DA, Rowe A, et al (2018) Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms19061578
  3. Zhang MM, Qiao Y, Ang E, Zhao H (2017) Using natural products for drug discovery: the impact of the genomics era. Expert Opinion on Drug Discovery. https://doi.org/10.1080/17460441.2017.1303478
  4. Cragg G, Newman D (2014) Natural products and drug discovery and development: A history of success and continuing promise for the future. Planta Medica. https://doi.org/10.1055/S-0034-1382292
  5. Tawfike AF, Viegelmann C, Edrada-Ebel R (2013) Metabolomics and dereplication strategies in natural products. Methods in molecular  biology. https://doi.org/10.1007/978-1-62703-577-4_17
  6. Avalon NE, Murray A, Baker B (2022) Integrated Metabolomic–Genomic Workflows Accelerate Microbial Natural Product Discovery. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.2c02245
  7. Laraia L, Waldmann H (2017) Natural product inspired compound collections: evolutionary principle, chemical synthesis, phenotypic screening, and target identification. Drug Discovery Today: Technologies. https://doi.org/10.1016/j.ddtec.2017.03.003
  8. Mullowney M, Duncan KR, Elsayed S, et al (2023) Artificial Intelligence For Natural Product Drug Discovery. Nature reviews Drug discovery. https://doi.org/10.1038/s41573-023-00774-7
  9. Mangla K, Jahan RN (2019) Pharmacognosy: Natural products in drug discovery. The Pharma Innovation. https://doi.org/10.22271/tpi.2019.v8.i4s.25511

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