Health
Could LLMs Revolutionize Drug and Material Design?
These researchers have developed an innovative system that augments an LLM with graph-based AI models, designed specifically to handle molecular structures

A new method is changing the way we think about molecule design, bringing us closer to the possibility of using large language models (LLMs) to streamline the creation of new medicines and materials. Imagine asking, in plain language, for a molecule with specific properties, and receiving a comprehensive plan on how to synthesize it. This futuristic vision is now within reach, thanks to a collaboration between researchers from MIT and the MIT-IBM Watson AI Lab.
A New era in molecular discovery
Traditionally, discovering the right molecules for medicines and materials has been a slow and resource-intensive process. It often involves the use of vast computational power and months of painstaking work to explore the nearly infinite pool of potential molecular candidates. However, this new method, blending LLMs with other machine-learning models known as graph-based models, offers a promising solution to speed up this process.
These researchers have developed an innovative system that augments an LLM with graph-based AI models, designed specifically to handle molecular structures. The approach allows users to input natural language queries specifying the desired molecular properties, and in return, the system provides not only a molecular design but also a step-by-step synthesis plan.
LLMs and graph models
LLMs like ChatGPT have revolutionized the way we interact with text, but they face challenges when it comes to molecular design. Molecules are graph structures—composed of atoms and bonds—which makes them fundamentally different from text. LLMs typically process text as a sequence of words, but molecules do not follow a linear structure. This discrepancy has made it difficult for LLMs to understand and predict molecular configurations in the same way they handle sentences.
To bridge this gap, MIT’s researchers created Llamole—a system that uses LLMs to interpret user queries and then switches between different graph-based AI modules to generate molecular structures, explain their rationale, and devise a synthesis strategy. The system combines the power of text, graphs, and synthesis steps into a unified workflow.
As a result, this multimodal approach drastically improves performance. Llamole was able to generate molecules that were far better at meeting user specifications and more likely to have a viable synthesis plan, increasing the success rate from 5 percent to 35 percent.
Llamole’s success lies in its unique ability to seamlessly combine language processing with graph-based molecular modeling. For example, if a user requests a molecule with specific traits—such as one that can penetrate the blood-brain barrier and inhibit HIV—the LLM interprets the plain-language request and switches to a graph module to generate the appropriate molecular structure.
This switch occurs through the use of a new type of trigger token, allowing the LLM to activate specific modules as needed. The process unfolds in stages: the LLM first predicts the molecular structure, then uses a graph neural network to encode the structure, and finally, a retrosynthetic module predicts the necessary steps to synthesize the molecule. The seamless flow between these stages ensures that the LLM maintains an understanding of what each module does, further enhancing its predictive accuracy.
“The beauty of this is that everything the LLM generates before activating a particular module gets fed into that module itself. The module is learning to operate in a way that is consistent with what came before,” says Michael Sun, an MIT graduate student and co-author of the study.
Simplicity meets precision
One of the most striking aspects of this new method is its ability to generate simpler, more cost-effective molecular structures. In tests, Llamole outperformed other LLM-based methods and achieved a notable 35 percent success rate in retrosynthetic planning, up from a mere 5 percent with traditional approaches. “On their own, LLMs struggle to figure out how to synthesize molecules because it requires a lot of multistep planning. Our method can generate better molecular structures that are also easier to synthesize,” says Gang Liu, the study’s lead author.
By designing molecules with simpler structures and more accessible building blocks, Llamole could significantly reduce the time and cost involved in developing new compounds.
The road ahead
Though Llamole’s current capabilities are impressive, there is still work to be done. The researchers built two custom datasets to train Llamole, but these datasets focus on only 10 molecular properties. Moving forward, they hope to expand Llamole’s capabilities to design molecules based on a broader range of properties and improve the system’s retrosynthetic planning success rate.
In the long run, the team envisions Llamole serving as a foundation for broader applications beyond molecular design. “Llamole demonstrates the feasibility of using large language models as an interface to complex data beyond textual description, and we anticipate them to be a foundation that interacts with other AI algorithms to solve any graph problems,” says Jie Chen, a senior researcher at MIT-IBM Watson AI Lab.
With further refinements, Llamole could revolutionize fields from pharmaceuticals to material science, offering a glimpse into the future of AI-driven innovation in molecular discovery.
Health
Researchers Develop Low-Cost Sensor for Real-Time Detection of Toxic Sulfur Dioxide Gas
Sulfur dioxide, a toxic air pollutant primarily released from vehicle exhaust and industrial processes, is notorious for triggering respiratory irritation, asthma attacks, and long-term lung damage.

In a significant breakthrough for environmental monitoring and public health, scientists from the Centre for Nano and Soft Matter Sciences (CeNS), Bengaluru, India, have developed an affordable and highly sensitive sensor capable of detecting sulfur dioxide (SO₂) gas at extremely low concentrations.
Sulfur dioxide, a toxic air pollutant primarily released from vehicle exhaust and industrial processes, is notorious for triggering respiratory irritation, asthma attacks, and long-term lung damage. Monitoring its presence in real time is essential, but existing technologies are often expensive, power-hungry, or ineffective at detecting the gas at trace levels.
To address this gap, the CeNS team, under the leadership of Dr. S. Angappane, has engineered a novel sensor by combining two metal oxides — nickel oxide (NiO) and neodymium nickelate (NdNiO₃). NiO serves as the receptor that captures SO₂ molecules, while NdNiO₃ acts as a transducer that converts the chemical interaction into an electrical signal. This innovative design enables the sensor to detect SO₂ at concentrations as low as 320 parts per billion (ppb), outperforming many commercial alternatives.
Speaking about the development, Dr. Angappane said in a media statement, “This sensor system not only advances the sensitivity benchmark but also brings real-time gas monitoring within reach for a wider range of users. It demonstrates how smart materials can provide practical solutions for real-world environmental challenges.”

The CeNS team has also built a portable prototype incorporating the sensor. It features a user-friendly threshold-triggered alert system with color-coded indicators: green for safe levels, yellow for warning, and red for danger. This visual approach ensures that even non-specialist users can understand and respond to pollution risks instantly. Its compact size and lightweight design make it ideal for deployment in industrial zones, urban neighborhoods, and enclosed environments requiring continuous air quality surveillance.
The sensor system was conceptualized and designed by Mr. Vishnu G Nath, with key contributions from Dr. Shalini Tomar, Mr. Nikhil N. Rao, Dr. Muhammed Safeer Naduvil Kovilakath, Dr. Neena S. John, Dr. Satadeep Bhattacharjee, and Prof. Seung-Cheol Lee. The research findings were recently published in the journal Small.
With this innovation, CeNS reinforces the role of advanced materials science in developing cost-effective technologies that protect both public health and the environment.
Health
Researchers Unveil 50-Cent DNA Sensors That Could Revolutionize Disease Diagnosis
The innovation lies in a low-cost electrochemical sensor stabilized with a polymer coating, which allows the device to be stored for months at high temperatures and used far from traditional lab settings

In a breakthrough that could make life-saving diagnostics accessible to millions, MIT researchers have developed a disposable, DNA-coated sensor capable of detecting diseases like cancer, HIV, and influenza — all for just 50 cents. The innovation lies in a low-cost electrochemical sensor stabilized with a polymer coating, which allows the device to be stored for months at high temperatures and used far from traditional lab settings.
At the heart of this sensor is a CRISPR-based enzyme system. When the sensor detects a target disease gene, the enzyme — acting like a molecular lawnmower — begins to shred DNA on the electrode, disrupting the electric signal and indicating a positive result.
“Our focus is on diagnostics that many people have limited access to, and our goal is to create a point-of-use sensor,” said Ariel Furst, MIT chemical engineering professor and senior author of the study, in a media statement. “People wouldn’t even need to be in a clinic to use it. You could do it at home.”
Previously, such sensors faced a major hurdle: the DNA coating degraded rapidly, requiring immediate use and refrigerated storage. Furst’s team overcame this by using polyvinyl alcohol (PVA) — a cheap and widely available polymer — to form a protective film over the DNA, significantly extending shelf life.
The sensors were tested to successfully detect PCA3, a prostate cancer biomarker found in urine, even after two months of storage at 150°F. The technology builds on Furst’s earlier work that enabled detection of HIV and HPV genetic material using similar CRISPR-based methods.
“This is the same core technology used in glucose meters, but adapted with programmable DNA,” said lead author Xingcheng Zhou, an MIT graduate student. “It’s inexpensive, portable, and extremely versatile.”
The team now aims to expand testing for other infectious and emerging diseases. They’ve been accepted into MIT’s delta v venture accelerator, signaling commercial interest and real-world application potential. The ability to ship sensors without refrigeration could be transformative for low-resource and remote settings.
“Our limitation before was that we had to make the sensors on site,” added Furst. “Now that we can protect them, we can ship them. That allows us to access a lot more rugged or non-ideal environments for testing.”
With further development, these pocket-sized DNA sensors could redefine early disease detection — from rural clinics to living rooms.
Health
Teak Leaf Extract Emerges as Eco-Friendly Shield Against Harmful Laser Rays
Raman Research Institute scientists unlock sustainable alternative for laser safety in line with green tech goals

In a significant step toward sustainable photonic technologies, scientists from the Raman Research Institute (RRI), an autonomous institute under the Department of Science and Technology (DST), Government of India, have discovered that teak leaf extract can serve as an effective, natural shield against harmful laser radiation. This breakthrough offers new potential for protecting both sensitive optical sensors and human eyes from high-intensity lasers used in medical, industrial, and defense applications.
The team has found that the otherwise discarded leaves of the teak tree (Tectona grandis L.f) are rich in anthocyanins, natural pigments responsible for their reddish-brown colour. When exposed to light, these pigments exhibit nonlinear optical (NLO) properties, allowing them to absorb intense laser beams—a key feature required for laser safety gear.
The discovery, recently published in the Journal of Photochemistry and Photobiology A: Chemistry, proposes a non-toxic, biodegradable, and cost-effective alternative to conventional synthetic materials like graphene and metal nanoparticles, which are often expensive and environmentally hazardous.
“Teak leaves are a rich source of natural pigments such as anthocyanin… We explored the potential of teak leaf extract as an eco-friendly alternative to synthetic dyes in the field of nonlinear optics,” said Beryl C, DST Women Scientist at RRI, in a media statement issued by the government.
To extract this natural dye, researchers dried and powdered teak leaves, soaked them in solvents, and processed the mixture using ultrasonication and centrifugation. The resulting reddish-brown liquid was then tested with green laser beams under continuous and pulsed conditions.
Using advanced techniques like Z-scan and Spatial Self-Phase Modulation (SSPM), the dye demonstrated reverse saturable absorption (RSA)—a rare and desirable trait where the material absorbs more light as the intensity increases, effectively acting as a self-regulating shield against laser exposure.
This development is particularly crucial as laser technologies become increasingly prevalent in everyday environments—from surgical devices and industrial cutters to military-grade systems. By offering a natural and renewable solution to a global safety challenge, the RRI team has opened the door to a future of eco-conscious optical safety equipment, such as laser-resistant eyewear, coatings, and sensor shields.
Researchers also indicated that further studies will focus on enhancing the stability and commercial usability of the dye for long-term deployment.
This innovation aligns with the principles of Industry 5.0, emphasizing human-centered and environmentally responsible technology, and showcases how indigenous, sustainable resources can play a pivotal role in global tech solutions.
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