How AI Revolutionized Protein Science, but Didn't End It
Artificial intelligence, exemplified by AlphaFold2 and AlphaFold3, revolutionized protein science by accurately predicting protein structures. Despite advancements, AI complements rather than replaces biological experiments, highlighting the complexity of simulating protein dynamics.
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Artificial intelligence (AI) made a significant impact on protein science when Google's AlphaFold2 presented a breakthrough in predicting protein structures with over 90% accuracy, revolutionizing the field. This achievement, announced in 2020, marked a turning point in understanding protein folding, a critical aspect of molecular biology with implications for disease research and drug development. Despite AI's success, it has not replaced the need for biological experiments but rather emphasized their importance. The subsequent release of AlphaFold3 in 2024 further advanced biological predictions by modeling protein structures in conjunction with other molecules like DNA or RNA. While AI has inspired new algorithms and biotech companies, there are still limitations in simulating protein dynamics over time and within cellular contexts. The protein folding problem, a longstanding challenge in biology, has seen progress but remains complex. The story of AlphaFold's impact underscores the evolving role of AI in biological research and the ongoing quest to unravel the mysteries of protein structure and function.
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