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==Multidisciplinar Application==
Differentiable programming is making significant strides in various fields beyond its traditional applications. In healthcare and life sciences, for example, it's being used for deep learning in biophysics-based modelling of molecular mechanisms. This involves leveraging differentiable programming in areas such as protein structure prediction and drug discovery. These applications demonstrate the potential of differentiable programming in contributing to significant advancements in understanding complex biological systems and improving healthcare solutions.<ref>{{cite journal |last1=AlQuraishi |first1=Mohammed |last2=Sorger |first2=Peter K. |title=Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms |journal=Nature Methods |date=October 2021 |volume=18 |issue=10 |pages=1169–1180 |doi=10.1038/s41592-021-01283-4 |pmid=34608321 |pmc=8793939 }}</ref>
==See also==
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