The future of computational biology for macromolecules in the age of AI: challenges and opportunities
This article explores the future of computational biology for macromolecules in the AI era. It notes that more progress has occurred in the last five years than in the preceding five decades, making forecasting difficult. The piece discusses whether the field has plateaued or will continue rapid transformation of biochemistry, molecular biology, and medicine. It highlights the growing role of machine learning and deep learning in published research, and identifies key challenges that must be overcome for AI to make significant contributions. The author predicts that in 20 years, the field will be characterized by advances in accuracy, automation, integration, and explainability, with AI playing a central role.
Key quotes
We have seen more progress in computational biology for macromolecules in the last five years than we experienced in the five preceding decades.
It is possible that we have reached a plateau, and we will be stuck with similar problems as we have today.
It is also possible that general AI will take over, and all scientific endeavours will be conducted without human input.
The future of computational biology for macromolecules in 20 years is likely to be characterised by transformative advances in accuracy, automation, integration, and explainability, with AI playing a role in one form or another.
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