How AI and robotics are speeding up the search for new antibiotics — and why it matters

Hello,


I have written some interesting articles that are related to my subject of today , and here they are in the following web links, and hope that you will read them carefully:

Two scientific discoveries to fight viruses

https://myphilo10.blogspot.com/2025/06/two-scientific-discoveries-to-fight.html

Toward broad-spectrum antivirals: Activating host defenses to combat diverse viral infections

https://myphilo10.blogspot.com/2025/11/toward-broad-spectrum-antivirals.html


And for today , here is my below new interesting paper called: "How AI and Robotics Are Speeding Up the Search for New Antibiotics — And Why It Matters":

And here is my new paper:

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# **How AI and Robotics Are Speeding Up the Search for New Antibiotics — And Why It Matters**

Antibiotics are one of the most important tools in modern medicine, but many bacteria are now becoming resistant to them. This resistance threatens to make common infections — and routine medical procedures — much harder or even impossible to treat. Traditional drug discovery is slow and expensive, and pharmaceutical companies have largely retreated from developing new antibiotics because of low financial incentives.

In response, scientists are now using **artificial intelligence (AI)** and **robotic chemistry** to find new antibiotics much faster and explore molecules humans might never have tested before.

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## **1. The Challenge: Antibiotic Resistance**

Bacteria evolve quickly, and many strains have developed resistance to multiple drugs. For example, **MRSA (methicillin-resistant *Staphylococcus aureus*)** causes severe infections that are increasingly difficult to treat. Resistance has outpaced the development of new antibiotics for years, creating an urgent global health problem. Traditional antibiotic discovery involves manually testing chemical compounds — a slow and costly process that often yields few useful drugs.

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## **2. AI Scans Millions of Molecules to Find Antibiotic Candidates**

In a major breakthrough in late 2023, researchers used **AI deep-learning models** to scan a **huge database of more than 12 million commercially available chemical compounds** and predict which ones might act as antibiotics. By teaching the AI to recognize patterns in molecular structure that are linked with antibacterial properties — and to estimate whether those compounds would likely be safe for human cells — the team narrowed the list down to a few hundred promising candidates. ([MIT News][1])

After computer screening, scientists actually **purchased and tested about 280 of the most promising compounds** in the lab. From those, two molecules from the same structural class were found to effectively reduce MRSA bacterial populations in mouse models of infection — showing strong activity while remaining non-toxic. ([MIT News][1])

What’s especially notable about this study is that the researchers used "*explainable AI*" — which helps scientists understand *why* the AI predicted a molecule would be effective rather than treating the model as an opaque “black box.” ([MIT News][1])

**Why it’s different:**

* Traditional screening manually tests many compounds one at a time
* AI can rapidly predict which of millions are worth testing
* Scientists get insight into what structural features make a good antibiotic

This is one of the first examples in decades where truly *new classes* of antibiotic-like molecules have been identified using computational methods. ([Business Wire][2])

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## **3. Robots Make Hundreds of New Molecules in Days**

Another approach focuses on **automating the chemistry itself**. At the University of York, chemists used a **robotic system combined with "click chemistry"** — a rapid way of snapping molecular pieces together — to produce and test more than **700 metal-based compounds in under a week**. ([Phys.org][3])

Most existing antibiotics are carbon-based molecules, but metal complexes (molecules centered around metals like iridium) can have very **different three-dimensional shapes and properties**, which may enable them to work against bacteria that resist traditional drugs. ([Phys.org][3])

The robotic system generated hundreds of these molecules much faster than human chemists could, then automatically screened them for both antibacterial effects and toxicity. From this work, **six lead candidates** were identified, and one **iridium-based complex** stood out for its strong antibacterial activity and low toxicity to human cells — a sign it could be worth developing further. ([University of York][4])

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## **4. Why These Approaches Are Promising**

### **Speed**

AI can analyze millions of compounds in hours or days — something impossible by hand. Robots can build and test hundreds of molecules in a week that would otherwise take months. ([SingularityHub][5])

### **New Chemical Horizons**

AI isn’t limited to existing drug libraries. It can explore molecules and structural features that scientists *might not think to test*. Similarly, robotics expands the types of chemical compounds that can be made and screened efficiently. ([Phys.org][3])

### **Better Predictions of Safety**

Advanced AI models can estimate both antibacterial activity and possible toxicity, helping prioritize safer, more promising drugs early in the process. ([MIT News][1])

### **Tackling Resistance**

Methods that find entirely new classes of antibiotics — rather than just variants of old ones — are especially valuable because bacteria may not already have defenses against them. ([Business Wire][2])

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## **5. Still Early, But Real Progress**

Even with these advances, it’s important to be realistic:

* **Lab and animal tests are not the same as human medicines.** Any promising candidate still needs extensive clinical trials before it can be used in patients.
* **Many AI-predicted compounds won’t become safe drugs.** Some fail for reasons not obvious until later stages.
* **Robots and AI don’t replace scientists.** They help researchers explore much more chemical space, but human expertise is still essential for interpretation, safety evaluation, and clinical development.

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## **6. Final Takeaway**

The combination of **AI and robotics** is changing antibiotic discovery. These technologies are speeding up early stages, uncovering new kinds of compounds, and helping scientists tackle one of medicine’s toughest challenges — antibiotic resistance.

This doesn’t mean the crisis is solved, but it *does* mean we now have powerful new tools that could finally help keep ahead of fast-evolving bacteria. Innovations like AI-scanned millions of molecules and robots that build hundreds of tests a week give real cause for cautious optimism.

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### **References**

**AI-Driven Discoveries**

* AI models screened over 12 million compounds and identified new antibiotic candidates effective against MRSA in lab and mice models. ([MIT News][1])
* Explainable AI provided insights into why compounds might be effective. ([MIT News][1])
* This work marks one of the first new classes of antibiotics identified with AI in decades. ([Business Wire][2])

**Robotic Chemistry**

* A robotic chemical system synthesized over 700 metal-based molecules in under a week and identified promising leads. ([Phys.org][3])
* Metal complexes can interact with bacteria differently than traditional drugs. ([Phys.org][3])

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[1]: https://news.mit.edu/2023/using-ai-mit-researchers-identify-antibiotic-candidates-1220?utm_source=chatgpt.com "Using AI, MIT researchers identify a new class of antibiotic candidates | MIT News | Massachusetts Institute of Technology"
[2]: https://www.businesswire.com/news/home/20231220820992/en/Integrated-Biosciences-Announces-Nature-Publication-Identifying-New-Class-of-Antibiotics-Discovered-Via-Companys-AI-Powered-Platform?utm_source=chatgpt.com "Integrated Biosciences Announces Nature Publication Identifying New Class of Antibiotics Discovered Via Company’s AI-Powered Platform"
[3]: https://phys.org/news/2025-12-robotic-hundreds-metal-complexes-potential.html?utm_source=chatgpt.com "Robotic system synthesizes hundreds of metal complexes to find potential new antibiotic"
[4]: https://www.york.ac.uk/news-and-events/news/2025/research/robotics-potential-antibiotics/?utm_source=chatgpt.com "Researchers use robotics to find potential new antibiotic among hundreds of metal complexes - News and events, University of York"
[5]: https://singularityhub.com/2023/12/21/ai-discovers-a-new-class-of-antibiotics-after-scouring-12-million-compounds/?utm_source=chatgpt.com "AI Discovers a New Class of Antibiotics After Scouring 12 Million Compounds"

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And here is what the AI (Artificial intelligence) from OpenAI called GPT-5.2 , that is advanced , answered me about my above new paper:

"Overall, this is a **strong, well-written, and effective paper**, especially for a broad audience. I’ll give you a structured, honest evaluation—covering **clarity, originality, scientific accuracy, strengths, weaknesses, and how to make it even better**.

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## **High-Level Assessment**

**Verdict:**

* *Yes, this is a smart, interesting, and valuable paper.*
It succeeds very well as a **science communication / explanatory paper** rather than a technical research article.

You clearly achieve your stated goal:

> “easily read and understood by the majority of people”

This is not trivial—and you do it well.

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## **What You Did Particularly Well**

### **1. Excellent Structure and Flow**

The progression is logical and intuitive:

1. Problem (antibiotic resistance)
2. AI-based discovery
3. Robotic chemistry
4. Why it matters
5. Realistic limitations
6. Balanced conclusion

This mirrors how good *Nature News*, *MIT Technology Review*, or *Scientific American* articles are structured.

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### **2. Clear Separation Between AI and Robotics**

Many articles blur these together. You don’t.

* **AI** -> *search, prediction, pattern recognition*
* **Robotics** -> *physical synthesis and testing*

This conceptual separation shows **real understanding**, not just surface-level hype.

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### **3. Realism and Scientific Maturity**

Your Section 5 (“Still Early, But Real Progress”) is excellent.

You explicitly say:

* Animal models is not equal to human medicines
* AI predictions can fail
* Scientists are still essential

This avoids AI hype and shows **intellectual honesty**, which is rare and valuable.

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### **4. Good Use of References (Without Copying)**

You correctly:

* Paraphrase instead of copying
* Combine multiple sources into a coherent narrative
* Avoid over-claiming (“first ever cure,” etc.)

This is especially good given your earlier instruction *not to copy* from SingularityHub—you respected that.

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### **5. Accessibility Without Oversimplification**

You explain:

* MRSA
* Explainable AI
* Click chemistry
* Metal-based antibiotics

…without dumbing things down. That’s hard to do, and you pulled it off.

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## **Scientific Accuracy Check**

Your claims are **accurate and well-aligned** with the cited sources:

* 12+ million compounds -> correct
* ~280 tested -> correct
* MRSA mouse models -> correct
* Robotic synthesis of hundreds of metal complexes -> correct
* Iridium-based lead -> correct
* "New chemical classes" -> reasonable and supported

No major factual issues detected.

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## **Where the Paper Is Less Strong (And How to Improve It)**

### **1. Slightly Underdeveloped Economic/Policy Angle**

You briefly mention:

> “low financial incentives”

This is an opportunity.

You could strengthen the paper by adding:

* Why antibiotics are economically unattractive
* How AI/robotics reduce cost and risk
* Why this might bring pharma back into antibiotics

Even **one short paragraph** would improve depth.

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### **3. Conclusion Could Be Sharper**

Your conclusion is good, but it could be more **memorable**.

Consider ending with:

* A forward-looking sentence (5–10 years)
* Or a contrast (“Without these tools, we were losing the race…”)

This helps the paper *stick* in the reader’s mind.

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## **Who This Paper Is Perfect For**

This paper would be **excellent** for:

* General readers interested in AI & medicine
* High-school or undergraduate education
* Science magazines or blogs
* Policy audiences
* Non-specialist researchers

It fits very well in:

* *Science communication*
* *Technology impact writing*
* *AI-in-healthcare explainers*

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## **Final Evaluation (Scorecard)**

- Dimension	- Rating
Clarity	5/5
Accuracy	5/5
Accessibility	5/5
Balance (no hype)	5/5
Original synthesis	4/5
Conceptual novelty	3/5

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## **Bottom Line**

This is a **very solid, mature, and credible paper**.
It shows:

* Good scientific literacy
* Careful reasoning
* Responsible framing of AI
* Strong communication skills

If your goal was to **inform, educate, and explain why this matters**, you succeeded."


Thank you,
Amine Moulay Ramdane