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Large Language Models: Transforming the Field of Scientific Discovery

April 11, 2025

Large Language Models: Transforming the Field of Scientific Discovery

The recent Scope Large Language Models (LLMs) have been used as powerful tools for changing the way scientific research is conducted. These models have massive amounts of data trained within them, causing human-like responses and creating queries that respond to human nature. As LLM models mature even more, they become more of a thinking space for scientists implementing literature to experiment with planning-based projects. As these models evolve, a drastic amount of scientific discovery creates more access to better research and more knowledge.

Foundations of LLMS

LLM models are artificial intelligence systems trained on massive datasets. Their idea is to help create and automate easy activities. ChatGPT, in which various people use this LLM model for emails and essays, has been successful in the LLM atmosphere. As the linguistics have changed, the LLM’s dataset has been applied to it.

Scope of Scientific Discovery

As LLMs evolve, the scientific community has been growing, helping the chemistry, physics, and biology industries. For example, Gemini and Grok AI, incubated by Google and Twitter, understand various concepts of physics, which makes them a very reputable source of information. With fantastic datasets, various scientists are beginning to use them for research and other expertise.

Google has begun to incubate research for medicinal research. In a sense, many chemical compounds have been simulated through AI, making medicinal research easier. Various compounds are being created and put into medicine to treat patients. Many automated factories produce millions of combinations daily, allowing them to make more medicine for patients.

Contributions to Biology

In the scope of biology, LLMs are redefining research methodologies by assisting in fields ranging from genomics to ecology, transforming the way scientists look at research concepts within those fields. The ability of LLM models to analyze patterns and genetic sequences and summarize complex findings makes them a necessary tool for research.

  • Genomic Data Analysis: LLMs can decode massive genomic datasets, helping researchers identify rare patterns linked to traits or diseases.
  • Ecosystem and Evolution Modeling: LLMS processes environmental patterns and data to understand the impacts of climate change and how species affect environments.
  • Protein Folding: Due to the increasing sum of information passed through LLMs, various models can create vast predictions with remarkable accuracy, which creates more medicines and disease detection.

Contributions to Physics

LLMs make a vast contribution to physics. Various Machine Learning algorithms are used in the creation of these LLM models, which makes mathematical concepts such as linear algebra second nature to them. In Physics, various mathematical concepts and abstract reasoning methods are used by researchers, which takes a large sum of time; hence, the LLM model comes and helps mediate that issue.

  • Equation Solving and Abstract Reasoning: LLMs are trained to understand and manipulate theoretical physics models, which help with concepts such as quantum mechanics.
  • Automated Simulation: After computational simulations, physicists use LLMs to generate different simulating methods by giving different computations and suggestions.
  • Data Analysis: LLMs use different libraries of code for data analysis that are crunched into massive Excel documents or CSV files that physics can use to make equations.

Contributions to Chemistry

Of all the vast fields of science, chemistry stands out as the field that actively uses LLMs to its advantage. AI is currently shaping the future of molecular science, drug discovery, and design. LLMs augment the workflow of chemistry, transforming the methods of experimentation, documentation, and simulation.

  • Medicine Discovery: LLMs assist in finding potential medications by predicting molecular interactions. This can generate thousands of outcomes, which can be used to discover more medicines to cure more diseases.
  • Chemical Reaction Prediction: LLMs predict the outcomes of chemical reactions, which helps chemists find optimal conditions and other information when experimenting.
  • Polymer Design: Currently contributing to the design of polymers, nanomaterials, and semiconductors, which can increase the chances of durability and flexibility based on the use cases of the experimenter.

Future of LLMs’ Contribution to Science

As more information and LLMs progress, their science scope can be expanded. Since these models have massive datasets, they can be used to give predictions and simulations rather than simple assistance and computational processing. Based on LLM’s ability to perform natural language processing, it can broadly test categories of science that cost vast amounts of money and time.

There are still some challenges, as there are many debates about the ethics and transparency of these models, which can hinder research. However, as these models advance, they have more time to mature, which leads to vast amounts of innovation. More mature models can produce better solutions due to their vast dataset and reputability.

Large Language Models are not only a technical term but also a shift in how research and knowledge are produced.

References

Alley, E. C. (2019, October 21). Unified rational protein engineering with sequence-based deep representation learning. nature methods. https://www.nature.com/articles/s41592-019-0598-1

Gómez-Bombarelli, R. (2018, January 12). Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS. https://pubs.acs.org/doi/10.1021/acscentsci.7b00572

Schwaller, P. (2019, August 30). Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction PredictionArticle link copied! ACS Publications. https://pubs.acs.org/doi/10.1021/acscentsci.9b00576

Vaswani, A. (2017, June 12). Attention Is All You Need. arXiv. https://arxiv.org/abs/1706.03762

 

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.