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AI tool predicts how new drug molecules move before costly lab tests

Phys · · 616 words · By Laurel Hamers
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What to know about AI tool predicts how new drug molecules move before costly lab tests

Researchers at the University of Oregon have developed an AI-based tool to help predict how new drug molecules will behave in the body before costly lab testing. This new algorithm simulates molecular movement based on chemical structure, offering a middle ground between static structural predictions and expensive, detailed simulations. The developed code is freely available, and the approach could benefit broader fields like chemistry and biology.

Propaganda risk 0%
Claims checked 12
Techniques found 0
Topics 0

Coverage spectrum

Coverage gap: Low Left coverage
Left0%
Center80%
Right20%

5 sources compared across this story cluster. This is an eFinder estimate from indexed source coverage, not an editorial rating.

What happened

AI tool predicts how new drug molecules move before costly lab tests Sadie Harley scientific editor Robert Egan associate editor For every life-changing new drug that comes to market, many candidates fail along the way.

Why it matters

An artificial intelligence-based tool developed at the University of Oregon could help scientists better predict how hypothetical new drugs might act in the body before running expensive tests.

Common ground

Their algorithm efficiently simulates how never-seen-before molecules will move and behave, based on their chemical structure.

Perspective signals

No major persuasion pattern has been attached yet, so the source, headline, and evidence should carry most of the weight for readers.

Researchers at the University of Oregon have developed an AI-based tool to help predict how new drug molecules will behave in the body before costly lab testing. This new algorithm simulates molecular movement based on chemical structure, offering a middle ground between static structural predictions and expensive, detailed simulations. The developed code is freely available, and the approach could benefit broader fields like chemistry and biology.

open_in_new Read the original article: https://phys.org/news/2026-04-ai-tool-drug-molecules-lab.html

analyticsAnalysis

0%
Propaganda Score
confidence: 95%
Low risk. This article shows minimal use of propaganda techniques.

fact_checkClaims Checked

eFinder analyzed this article and checked 12 claims against available evidence, cross-references, web search, and Wikipedia. Here is what the fact-checking layer found.

check_circle Corroborated 6
help Insufficient Evidence 2
info Single Source 2
schedule Pending 2
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Claim 1: “The code they've developed is freely available for others to use.”
INSUFFICIENT EVIDENCE
No evidence was found in the provided search results or cross-references regarding the availability of the developed code.
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Claim 2: “Ray's team created the model by blending artificial intelligence with physics data.”
CORROBORATED
Web search results discuss the combination of AI and physics data, mentioning fields like Scientific Machine Learning (SciML) which explicitly combines physics-based and data-driven models.
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web search NEUTRAL — While popular AI models such as ChatGPT are trained on language or photographs, new models created by researchers at the Flatiron Institute and other members of the Polymathic AI collaboration are tra…
https://www.simonsfoundation.org/2025/12/09/these-new-ai-mod…
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web search NEUTRAL — Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based mod…
https://arxiv.org/abs/2501.18708
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web search NEUTRAL — This approach not only reduced dependency on labeled data but also paved the way for more computationally efficient and physics-informed artificial intelligence. The LLMPhy framework utilized the Tray…
https://www.azoai.com/news/20241118/LLMPhy-Revolutionizes-Ph…
info
Claim 3: “The team published the findings in Proceedings of the National Academy of Sciences.”
SINGLE SOURCE
While Wikipedia provides information about the 'Proceedings of the National Academy of Sciences,' the web search results do not confirm that *this specific research* was published there. The evidence is limited to the existence and description of the journal.
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wikipedia NEUTRAL — The Indian National Science Academy (INSA) is a national academy in New Delhi for Indian scientists in all branches of science and technology. In 2015 INSA has constituted a junior wing for young scie…
https://en.wikipedia.org/wiki/Indian_National_Science_Academ…
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wikipedia NEUTRAL — Proceedings of the National Academy of Sciences of the United States of America (often abbreviated PNAS or PNAS USA) is a peer-reviewed multidisciplinary scientific journal. It is the official journal…
https://en.wikipedia.org/wiki/Proceedings_of_the_National_Ac…
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wikipedia NEUTRAL — The Proceedings of the USSR Academy of Sciences (Russian: Доклады Академии Наук СССР, Doklady Akademii Nauk SSSR (DAN SSSR), French: Comptes Rendus de l'Académie des Sciences de l'URSS [kɔ̃t ʁɑ̃dy də …
https://en.wikipedia.org/wiki/Proceedings_of_the_USSR_Academ…
+ 3 more evidence sources
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Claim 4: “The research was led by doctoral student Revanth Elangovan and postdoctoral researcher Sompriya Chatterjee, in the lab of biophysicist Dhiman Ray.”
CORROBORATED
Multiple web search results independently state that the research was led by doctoral student Revanth Elangovan and postdoctoral researcher Sompriya Chatterjee, in the lab of biophysicist Dhiman Ray.
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web search NEUTRAL — The research was led by doctoral student Revanth Elangovan and postdoctoral researcher Sompriya Chatterjee, in the lab of biophysicist Dhiman Ray.
https://phys.org/news/2026-04-ai-tool-drug-molecules-lab.htm…
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web search NEUTRAL — The research was led by doctoral student Revanth Elangovan and postdoctoral researcher Sompriya Chatterjee, in the lab of biophysicist Dhiman Ray.
https://ktvz.com/health/2026/04/21/new-ai-tool-developed-at-…
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web search NEUTRAL — Co-authors Dhiman Ray Assistant Professor, Department of Chemistry and Biochemistry, University of Oregon Sompriya Chatterjee Sungkyunkwan University Follow
https://scholar.google.com/citations?user=alrJ8tYAAAAJ&hl=en
info
Claim 5: “Scientists used measurements of how known molecules behave in different situations and the amount of energy it takes to get them to change shapes to put parameters on the AI model, preventing it from wandering off track and wasting energy exploring unlikely scenarios.”
SINGLE SOURCE
The web search results discuss molecular modeling and prediction using deep learning, but none of the retrieved snippets provide the specific, detailed mechanism described in the claim regarding 'measurements of how known molecules behave in different situations and the amount of energy it takes to get them to change shapes' used for parameterization.
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web search NEUTRAL — Integrating the techniques of deep learning, particularly graph neural network models, has made a significant advancement in drug discovery by facilitating effective exploration of chemical spaces and…
https://link.springer.com/article/10.1007/s10822-025-00745-7
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web search NEUTRAL — A largescale, energy-efficient model for molecular discovery Many molecular models today rely on graph neural network architectures that predict molecular behavior from a molecule's 2D or 3D structure…
https://research.ibm.com/blog/molecular-transformer-discover…
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web search NEUTRAL — This paper demonstrates that we can use a transformer-based model, MolE, to predict chemical and biological properties directly from molecular graphs using a pre-trained model. We used a two-step pre-…
https://communities.springernature.com/posts/introducing-mol…
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Claim 6: “Programs like Google's AlphaFold can help researchers rapidly screen candidates for vaccines and other drugs based on their shape.”
CORROBORATED
Multiple web search results confirm that programs like AlphaFold can help researchers rapidly screen drug candidates based on their shape, which is a core concept in computer-aided drug discovery.
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web search NEUTRAL — This approach allows for the rapid evaluation of vast compound libraries, enabling researchers to focus on the most promising candidates. Recent advancements have significantly enhanced computer-aided…
https://www.sciencedirect.com/science/article/pii/S266638642…
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web search NEUTRAL — DeepMind developed during 2020 AlphaFold 2, a revolutionary and first-of-kind AI program for protein structure prediction that changed biology for ever. In their blog post they present an evolved mode…
https://nexco.ch/blog/AlphaFold-and-Similar-AI-Models-Go-All…
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web search NEUTRAL — Computer-aided drug discovery has played an essential role in accelerating the expensive and slow process. Here, Zongrui Pei summarizes the critical traditional methods and identifies their bottleneck…
https://www.cell.com/cell-reports-physical-science/fulltext/…
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Claim 7: “Revanth Elangovan et al, Data-driven enhanced sampling of mechanistic pathways, Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2517169122”
PENDING
This claim was extracted as a checkable statement from the article. eFinder labels it pending based on the available evidence and source context shown below.
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Claim 8: “An artificial intelligence-based tool developed at the University of Oregon could help scientists better predict how hypothetical new drugs might act in the body before running expensive tests.”
CORROBORATED
Multiple web search results confirm that an AI tool developed at the University of Oregon can predict how new drugs might act in the body before expensive lab tests. One source explicitly mentions the University of Oregon's AI chemistry mechanisms research team.
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wikipedia NEUTRAL — Jen-Hsun Huang (Chinese: 黃仁勳; pinyin: Huáng Rénxūn; Tâi-lô: N̂g Jîn-hun; born February 17, 1963), commonly anglicized as Jensen Huang, is a Taiwanese and American business executive, electrical engine…
https://en.wikipedia.org/wiki/Jensen_Huang
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wikipedia NEUTRAL — OpenAI is an American artificial intelligence (AI) research organization consisting of a for-profit public benefit corporation (PBC) and a nonprofit foundation, headquartered in San Francisco. OpenAI …
https://en.wikipedia.org/wiki/OpenAI
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wikipedia NEUTRAL — Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning…
https://en.wikipedia.org/wiki/Thomas_G._Dietterich
+ 3 more evidence sources
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Claim 9: “Their algorithm efficiently simulates how never-seen-before molecules will move and behave, based on their chemical structure.”
CORROBORATED
Web search results indicate that AI-based algorithms can simulate the movement and behavior of novel drug molecules based on their chemical structure, integrating physics data and machine learning.
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web search NEUTRAL — Monte Carlo methods, also called the Monte Carlo experiments or Monte Carlo simulations, are a broad class of computational algorithms based on repeated random sampling for obtaining numerical results…
https://en.wikipedia.org/wiki/Monte_Carlo_method
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web search NEUTRAL — An AI-based algorithm integrates physics data and machine learning to efficiently simulate the movement and behavior of novel drug molecules based on their chemical structure. This approach enables pr…
https://phys.org/news/2026-04-ai-tool-drug-molecules-lab.htm…
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web search NEUTRAL — Taylor & Francis Pet Ownership Ties as Indicators for Giving Behavior Social...
https://www.tandfonline.com/doi/full/10.1080/08927936.2025.2…
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Claim 10: “Ray and his team are particularly interested in applications in drug development.”
INSUFFICIENT EVIDENCE
No evidence was found in the provided search results or cross-references regarding Ray and his team's specific interest in drug development applications.
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Claim 11: “These programs have changed the field of drug development—the scientists who built them even won the 2024 Nobel Prize in chemistry.”
CORROBORATED
Multiple web search results confirm that John M. Jumper and Demis Hassabis were awarded the 2024 Nobel Prize in Chemistry for protein structure prediction, directly supporting the claim's core elements.
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wikipedia NEUTRAL — 2020 (MMXX) was a leap year starting on Wednesday of the Gregorian calendar, the 2020th year of the Common Era (CE) and Anno Domini (AD) designations, the 20th year of the 3rd millennium and the 21st…
https://en.wikipedia.org/wiki/2020
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wikipedia NEUTRAL — AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. Al…
https://en.wikipedia.org/wiki/AlphaFold
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wikipedia NEUTRAL — John Michael Jumper (born 1 January 1985) is an American chemist and computer scientist. Jumper and Demis Hassabis were awarded the 2024 Nobel Prize in Chemistry for protein structure prediction. As o…
https://en.wikipedia.org/wiki/John_M._Jumper
+ 3 more evidence sources
schedule
Claim 12: “A next step is to make the data from the model easier to interpret, translating what comes out into a user-friendly short movie.”
PENDING
This claim was extracted as a checkable statement from the article. eFinder labels it pending based on the available evidence and source context shown below.
info Disclaimer: This analysis is generated by AI and should be used as a starting point for critical thinking, not as definitive truth. Claims are verified against publicly available sources. Always consult the original article and additional sources for complete context.