---
title: "Autonomous Drug Discovery: Mining 700,000 Natural Products for Antimicrobial Candidates"
description: "How K-Dense Web autonomously processed the COCONUT database to identify 50 prioritized antimicrobial candidates using unsupervised machine learning."
publishedAt: "2026-01-17"
tags: ["Use Case", "Machine Learning", "Research", "AI", "Drug Discovery"]
canonical: "https://k-dense.ai/blog/antimicrobial-drug-discovery-natural-products"
---
The global antibiotic pipeline is in bad shape. Bacterial infections that were reliably treatable 30 years ago now kill people, and new antibiotic approvals have been slow. Natural products - compounds produced by bacteria, fungi, and plants - have historically been the best source of new ones. Penicillin, vancomycin, erythromycin: all came from organisms that had already figured out how to kill bacteria.

The problem is there are a lot of natural products to look through. The COCONUT database alone has 715,822 of them.

In this case study, K-Dense Web processed that entire database to produce 50 prioritized antimicrobial candidates ready for experimental screening. The whole pipeline ran in about 45 minutes.

## Finding needles in a molecular haystack

Manually evaluating 715,000+ compounds isn't realistic. You need a way to filter, cluster, and prioritize before anything goes near a lab.

K-Dense Web was given a single prompt describing the research goal, then designed and ran the rest.

## The pipeline

![Autonomous workflow schematic showing the complete pipeline from data preparation through candidate selection](./cover.png)

### Step 1: Data preparation

K-Dense Web downloaded the full COCONUT database (664 MB), filtered for bacterial-derived compounds, and validated all SMILES structures using RDKit.

- Compounds downloaded: 715,822
- Bacterial-derived compounds: 24,911 (3.48%)
- Validation rate: 99.996%
- Final dataset: 24,910 unique compounds with validated structures

### Step 2: Feature engineering

For each compound, K-Dense Web calculated physicochemical properties (molecular weight, LogP, TPSA, hydrogen bond donors/acceptors), structural descriptors (ring count, aromatic rings, fraction sp3 carbons), and drug-likeness metrics (QED score, Lipinski's Rule of 5 compliance, PAINS filtering).

| Property | Mean | Range |
|----------|------|-------|
| Molecular Weight | 539 Da | 1 - 4,900 Da |
| LogP | 2.1 | -29 to 37 |
| QED Score | 0.36 | 0.01 - 0.94 |
| Lipinski Compliant | 44.8% | - |

Only 39.7% of compounds passed both Lipinski's Rule of 5 and PAINS filters. That's not surprising - bacterial natural products often sit outside traditional drug-like chemical space. Vancomycin and daptomycin wouldn't pass Lipinski either.

### Step 3: Chemical space analysis

The original plan was to pull bioactivity training data from ChEMBL and build a supervised model. The ChEMBL API returned errors. Rather than stopping, K-Dense Web switched to an unsupervised approach - which worked fine.

PCA of the full compound set turned up two distinct clusters:

![PCA visualization showing two distinct chemical clusters in the bacterial natural product space](./chemical-space-clusters.png)

**Cluster 0 (75.4% of compounds):** Small, drug-like molecules. Mean MW: 396 Da, mean QED: 0.44. Probably alkaloids, terpenoids, and smaller polyketides.

**Cluster 1 (24.6% of compounds):** Large, complex molecules. Mean MW: 978 Da - 2.5× larger - mean QED: 0.09. Probably glycopeptides, lipopeptides, and macrocyclic antibiotics.

This bimodal split maps cleanly onto what's already known about antimicrobial natural products: one population of small, simple molecules and another of the kind of complex scaffolds that produced vancomycin.

![Heatmap showing normalized chemical profiles for each cluster](./cluster-profiles.png)

### Step 4: Candidate selection

K-Dense Web selected 25 compounds from each cluster:

**Group A: Drug-like leads (from Cluster 0)**
- Mean MW: 322 Da, mean QED: 0.93
- 100% Lipinski compliant
- Good starting points for traditional medicinal chemistry optimization

**Group B: Complex scaffolds (from Cluster 1)**
- Mean MW: 1,930 Da, mean QED: 0.05
- Structural types typical of clinically successful antibiotics
- Lower development tractability, higher novelty potential

![PCA plot showing the 50 selected candidates highlighted against the full dataset](./candidate-selection.png)

Covering both groups hedges the bets. Group A is easier to develop and optimize. Group B is where the bigger swings are.

### Step 5: Validation and reporting

K-Dense Web produced 10 publication-ready figures, a research manuscript with methods, results, and discussion sections, and detailed candidate profiles.

![Property distribution comparison between Group A and Group B candidates](./property-comparison.png)

The two groups look quite different from each other:

![Chemical structures of top candidates from each group](./top-candidates.png)

## Results

| Metric | Value |
|--------|-------|
| Initial compounds screened | 715,822 |
| Bacterial compounds identified | 24,910 |
| Chemical clusters discovered | 2 |
| Prioritized candidates | 50 |
| Group A (drug-like) | 25 |
| Group B (complex) | 25 |
| Pipeline execution time | ~45 minutes |

## What this workflow replaced

Traditional computational drug discovery involves real setup time: installing RDKit, figuring out ChEMBL's API, writing clustering code, debugging visualization scripts. When something breaks mid-analysis - like an API going down - you lose time replanning.

K-Dense Web ran the full pipeline, handled the API failure without stopping, and produced figures and a manuscript draft at the end. The 45-minute runtime is mostly compute, not planning or debugging.

The 50 candidates are ready for antimicrobial screening against resistant strains (MRSA, VRE, MDR pathogens), MIC determination, and structure-activity relationship analysis using the chemical space clusters.

## Try it yourself

[Start your autonomous research project on K-Dense Web →](https://app.k-dense.ai)

---

*This case study was generated from K-Dense Web. [View the complete example session](https://app.k-dense.ai/share/session_20251212_135154_dad1cf0d70ee) including all analysis code, data files, figures, and the publication-ready research manuscript.*