Application of Artificial Intelligence in Biochemistry Research: A Review
DOI:
https://doi.org/10.54987/jobimb.v11i2.835Keywords:
Artificial Intelligence, Biochemistry Research, Data, Genomics, Protein foldingAbstract
The exploration of Biochemistry research has been expanded by artificial intelligence (AI) and its ability to analyse immense and intricate datasets in a way that would be unattainable by human effort alone. This review delves into the most recent examples of AI breakthroughs that had a transformative impact on key aspects of biochemistry. AI has now led to the creation and improvement of drug molecules and the capability to predict which new proteins could be targeted for repurposing with current drugs. When it comes to protein structures, algorithms such as AlphaFold have made great strides in resolving the protein folding problem that has been a challenge for so long. Reliably identifying proteins and metabolites from spectral data is now possible with deep learning models. Meanwhile, AI can classify sequences and spot gene expression patterns in massive genomics and transcriptomics datasets with ease. The remarkable capabilities of AI to automate the analysis of medical images and natural language descriptions of patient symptoms have a promising potential for transforming disease diagnosis and treatment. Nevertheless, obstacles such as data availability, interpretability of AI models, ethical considerations, and generalization must be tackled as these technologies evolve. The collaboration between AI and biochemistry appears to be optimistic, with biochemical data powering the development of more robust AI systems that can extract new knowledge from vast datasets beyond human reach. Thus, this mutually beneficial relationship has the potential to vastly expedite discovery across molecular biology.
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