The AI Revolution: Predicting Protein Structures, Helping in Drug Discovery and Finding Battery Materials

Tue Jan 28 2025
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Key points

  • AI is revolutionising scientific research by accelerating discoveries across various fields
  • AlphaFold can accurately determine the three-dimensional shape of proteins
  • AI reduces gap between disease understanding and identification of therapeutic agents
  • AI leads to innovations in battery technology and renewable energy solutions

ISLAMABAD: Artificial Intelligence (AI) is changing as well as revolutionising scientific research by accelerating discoveries across various fields. Recent advancements highlight AI’s transformative role in understanding complex biological structures, helping in drug development, and enhancing material science.

In a landmark achievement, Demis Hassabis and John Jumper of Google DeepMind, along with biochemist David Baker, were awarded the 2024 Nobel Prize in Chemistry for their work on AlphaFold.

Their AI programme almost accurately predicts protein structures. It was a challenge that has confused scientists for several years.

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Image Credit: Google DeepMind

AlphaFold’s, on the other hand, capability to determine the three-dimensional shapes of proteins has profound implications for biology and medicine, helping in vaccine development and disease understanding.

Demis Hassabis said, “I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people. AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery.”

Drug Discovery

Moreover, AI is enhancing the drug discovery process by showing how proteins fold. It is important for understanding diseases and developing treatments.

In the past, determining a protein’s structure was a time-consuming effort for scientists and researchers, however, AI models such as AlphaFold can predict these structures rapidly. It can enable researchers to design drugs more efficiently.

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Image Credit: iStock

Ashfaqur Rehman of Shanghai Jiao Tong University School of Medicine writes in his research article, “The application of artificial intelligence (AI) in medicine, particularly through machine learning (ML), marked a significant progression in drug discovery. AI acts as a powerful catalyst in narrowing the gap between disease understanding and the identification of potential therapeutic agents.”

Material Science

Beyond biology, AI is also making a lot of contributions to material science.

Researchers are now using AI to predict the properties of new materials, leading to innovations in battery technology and renewable energy solutions. This can help adapt to climate change a lot easier than previously anticipated.

AI tools can analyse huge datasets. Its models can also identify promising material combinations, accelerating the development of more efficient batteries and sustainable materials.

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AI research can improve EV batteries. Photo courtesy of Pixabay and Wikimedia Commons

Anand Ramachandran, a US-based researcher writes in his research article, “AI models like Crystal Graph Neural Networks (CGNNs) and Reinforcement Learning (RL) frameworks enable researchers to simulate thousands of material combinations and predict their electrochemical properties. This reduces the reliance on costly and time-intensive laboratory experiments.”

He added, “AI-powered predictive maintenance systems are revolutionising battery lifecycle management, ensuring safety and efficiency by monitoring real-time data on battery health.”

AI is also leading to collaboration among scientists by identifying potential research directions along with finding experts in various fields.

AI, by analysing publication data and research trends, can suggest fruitful areas of study and connect researchers with complementary expertise, thereby accelerating the pace of scientific discovery.

Ethical Considerations

AI’s contributions to science are profound and appreciable, but they also raise some serious ethical considerations. The reliance on large datasets necessitates careful handling of data privacy and security.

Moreover, the integration of AI into research workflows requires transparency to ensure that findings are interpretable and trustworthy.

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As AI continues to grow and evolve, establishing guidelines for its responsible use in scientific research is necessary.

The future of AI in scientific discovery is promising. Current research is trying to increase AI’s capabilities in modelling complex systems, from simulating climate change scenarios to understanding complex biological networks.

As AI models become more advanced, their ability to generate new hypotheses and design experiments autonomously could redefine the scientific method itself.

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