Bioretrosynthesis

Bioretrosynthesis is a computational biology technique that uses machine learning and artificial intelligence to predict the biosynthetic pathway of a given molecule. This technique is particularly useful in the field of synthetic biology, where it can aid in the design of microorganisms for the production of desired chemicals.

Overview[edit | edit source]

Bioretrosynthesis was first proposed by Connor W. Coley, Justin B. Siegel, and Klavs F. Jensen in 2019. The technique involves the use of machine learning algorithms to predict the biosynthetic pathway of a given molecule. This is achieved by training the algorithm on a large dataset of known biosynthetic pathways, allowing it to learn the patterns and rules that govern these pathways.

Once the algorithm has been trained, it can be used to predict the biosynthetic pathway of a new molecule. This involves breaking down the molecule into its constituent parts, and then predicting the sequence of enzymatic reactions that could have produced these parts. The predicted pathway can then be used to engineer a microorganism that is capable of producing the desired molecule.

Applications[edit | edit source]

Bioretrosynthesis has a wide range of potential applications in the field of synthetic biology. For example, it could be used to design microorganisms that are capable of producing useful chemicals, such as pharmaceuticals, biofuels, and industrial chemicals. This could potentially reduce our reliance on fossil fuels and help to create a more sustainable and environmentally friendly chemical industry.

See also[edit | edit source]

Bioretrosynthesis Resources
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