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Washington Post: AI's Impact On Chemistry, Biomedicine Is Astounding

One of this year's Nobel Prize winners in physics, Geoffrey Hinton, who pioneered work on the neural networks that undergird artificial intelligence, has warned that machines might someday get smarter than humans. Perhaps. But this year's Nobel Prize in chemistry honored a real-world example of how AI is helping humans today with astounding discoveries in protein structure that have far-reaching applications. This is a development worth savoring.

Proteins are biology's lead actors. As the Nobel committee pointed out, proteins "control and drive all the chemical reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues." In the human body, they are necessary for the structure, function and regulation of tissues and organs. All proteins begin with a chain of up to 20 kinds of amino acids, strung together in a sequence encoded in DNA. Each chain folds into a unique structure, and those shapes determine how proteins interact with other molecules.

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Looking like a tangled ball of twine, proteins have a complex and precise design of moving parts that are linked to chemical events and bind to other molecules. Antibodies are proteins produced by the immune system that bind to foreign molecules, including those on the surface of an invading virus, such as the spikes on the coronavirus that causes COVID-19.

At the end of the 1950s, University of Cambridge researchers John Kendrew and Max Perutz successfully used a method called X-ray crystallography to produce the first 3D models of proteins. In recognition, they were awarded the 1962 Nobel Prize in chemistry. In the ensuing half-century, the quest to document protein structures remained laborious and slow. A single protein structure might take a doctoral student four or five years to figure out. Before AI, the field's central repository contained some 185,000 experimentally solved protein structures.

This year's Nobel Prize in chemistry went to three scientists who revolutionized the field. David Baker of the University of Washington built entirely new kinds of proteins. Demis Hassabis and John Jumper of DeepMind, a Britain-based firm that is part of Alphabet, Google's parent company, developed an AI and machine-learning model that can predict the structure of proteins, decoding the amino acids that make up each. The model, AlphaFold, can do in minutes what once took years.

AlphaFold takes advantage of neural networks that can locate patterns in enormous amounts of data. The system was trained on the vast information in the databases of all known protein structures and amino acid sequences. AlphaFold has predicted more than 200 million protein structures, or nearly all catalogued proteins known to science, including those in humans, plants, bacteria, animals and other organisms. The AlphaFold Protein Structure Database makes this data freely available.

To design new drugs and vaccines, scientists need to know how a protein looks or behaves. The AlphaFold result is a prediction that can accelerate biomedical research. ...

The AlphaFold blog recounts the story of scientists searching for a better vaccine against malaria, a disease that afflicts 250 million people a year and causes more than 600,000 deaths. Because malaria is caused by a shape-shifting parasite, vaccine researchers had long struggled to characterize the structure of one surface protein they needed to target to interrupt the infection. Then AlphaFold's prediction of the right structure snapped it into focus. Matthew Higgins at the University of Oxford said the breakthrough helped his team decide which bits of the protein to put in the vaccine, which trains the body's immune system to detect it and act. This helped advance his research from "a fundamental science stage to the preclinical and clinical development stage."

Anyone who has used ChatGPT knows that AI is not always correct — and the malaria scientists found that some of the 3D visualizations of proteins were inexact. But AI will only get better over time. Already, the AlphaFold effort is expanding to create accurate visualizations of how proteins interact with other biomedical structures, such as nucleic acids.

In the years ahead, AI dangers must be confronted and safeguards considered. Without a doubt, there are risks when powerful technology falls into the hands of malign actors.

But, for now, AlphaFold shows that AI can supercharge existing knowledge to benefit mankind. The Nobel committee noted that, thanks to these advances, "researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic." And there will be more to come.

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Did AI Just Win The Nobel Prizes In Physics And Chemistry?

As every year, there was a lot of excitement for the Nobel prizes last month. Scrolling through X, what stood out for me were the comments about the winners for physics and chemistry. While all were laudatory about the researchers and their work, many pointed out that these prizes were not really a recognition of their own achievements but of something else entirely: artificial intelligence (AI).

Indeed, it is hard to overstate the growing importance of AI not just in science and technology, but in society at large. According to a recent report published by the European Research Council, AI has applications in fields such as health, transport, manufacturing, food and farming, public administration, education and cybersecurity. The technological applications of AI have vast effects, with some computer scientists proclaiming dystopian changes to humanity's future.

Also very important is the impact of AI in chemistry and biochemistry where, among other things, it is being used to synthesise novel chemical substances for the purpose of designing new drugs. Some argue that AI could lead to immense progress in chemistry and revolutionise chemical practice.

But is this a sensible expectation? Is AI really going to change chemistry in ways that we cannot imagine? Will it revolutionise how we understand the world and our place in it? These sound like daunting questions but what they really come down to are the same old questions philosophers of science have been asking for centuries: how does science progress, and what constitutes a scientific revolution?

Traditionally, certain values are used to evaluate whether progress is achieved in a science. Two of the most significant ones are the ability of a science to make novel predictions and its ability to offer coherent explanations of phenomena. In fact, before the use of AI in science, it was widely accepted that the predictive and explanatory success of a science indicates the truth of the underlying theory, and acts as a criterion for the choice of one theory over another.1

How exactly ML algorithms produce useful and empirically successful results is quite obscure

AI seems to challenge this widely held assumption as its algorithms make predictions and offer explanations of phenomena without using theoretical postulates, laws or hypotheses as inputs.2 Instead, machine learning (ML) algorithms 'learn' from data about the systems they are set to describe and discover patterns, which are in turn used to make predictions about similar systems. How exactly ML algorithms produce useful and empirically successful results is quite obscure; in philosophy, this question is called 'the problem of opacity'.3

The problem of opacity also reveals another thorn to the idealised image we have of AI. How autonomous is it really? By this, I do not mean as an entity (though questions about artificial personhood have been raised around AI), but rather I refer to AI's capacity to produce accurate and useful results without the scrutiny of the scientific eye.

A colleague of mine once told me that nowadays, some biology laboratories contain people who have no idea about biology whatsoever. Something similar is reflected in this year's Nobel prize winners. One of the three winners of the prize in chemistry is a computer scientist with no background in chemistry! How strange, and perhaps a bit unsettling. However, I believe it also reflects a misconception we have about the role of AI in science. Without AI and the development of ML algorithms by computer scientists, these amazing scientific achievements would not have been possible. But do these achievements mean anything unless there is a scientist who knows not only how to use them but also, more importantly, how to evaluate them?

Let me give you a mundane example. I was expressing my worries recently to a colleague about how I am going to evaluate a philosophy course this semester. Usually, I would assign students a paper for them to write a summary about. Now, I told her, I'm not so sure this has any value. Students could use some AI tool and hand over a summary that an algorithm wrote for them.

She said I was mistaken. AI can be very wrong and produce summaries of texts that very poorly summarise their content. Of course, a student may not realise that when producing such a summary. But I would! And so would anyone who actually read the original paper.

So perhaps it is not the end of an era. Rightly we award prizes for achievements in chemistry and physics, even if not all recipients are experts in those fields. AI can do nothing without the expert eye of the scientist who scrutinises its results. A deep knowledge of the underlying theory and the constant experimental evaluation of all results are still very much – if not more – important for scientific progress to actually happen.

Long live the sciences then, and let's refrain from putting AI on a pedestal just yet!

AI's Impact On Science: Balancing Progress With Pitfalls

In a significant leap forward, artificial intelligence is now pivotal in the realms of chemistry and physics, with its influence being acknowledged by the 2024 Nobel Prize laureates. The ability of AI to accelerate research is transformative, yet it brings forth challenges that necessitate cautious implementation.

Experts caution against the illusions AI may create—misleading conclusions and potential bias—highlighting the necessity for rigorous scrutiny of its applications. While AI promises cheaper and faster science, it risks undermining trust in scientific integrity and flooding the landscape with low-value research outputs.

The scientific community faces the challenge of integrating AI responsibly, ensuring it meets societal needs and supports sustainable practices. Engaging in dialogues about these challenges and the potential of a renewed social contract is crucial to maximizing AI's benefits while mitigating its risks.

(With inputs from agencies.)

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