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Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms

Abstract

Deep learning using neural networks relies on a class of machine-learnable models constructed using ‘differentiable programs’. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, machine-learnable components trained on experimental data. Such programs are having a growing impact on molecular and cellular biology. In this Perspective, we describe an emerging ‘differentiable biology’ in which phenomena ranging from the small and specific (for example, one experimental assay) to the broad and complex (for example, protein folding) can be modeled effectively and efficiently, often by exploiting knowledge about basic natural phenomena to overcome the limitations of sparse, incomplete and noisy data. By distilling differentiable biology into a small set of conceptual primitives and illustrative vignettes, we show how it can help to address long-standing challenges in integrating multimodal data from diverse experiments across biological scales. This promises to benefit fields as diverse as biophysics and functional genomics.

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Fig. 1: Deep learning revolution.
Fig. 2: Neural network primitives.
Fig. 3: Differentiable programming fuses principles-based and data-driven modeling.
Fig. 4: Protein structure prediction vignette.
Fig. 5: PPI vignette.

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Acknowledgements

We thank N. Bouatta for comments on early versions of this manuscript. This work is supported by DARPA PANACEA program grant HR00111920022 and NCI/NIH grant U54-CA225088 to P.K.S.

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Correspondence to Mohammed AlQuraishi or Peter K. Sorger.

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P.K.S. is a member of the scientific advisory board or board of directors of Glencoe Software, Applied Biomath, RareCyte and NanoString and a consultant to Montai Health and Merck; he has equity in several of these companies. P.K.S. declares that none of these relationships are directly or indirectly related to the content of this manuscript. M.A.Q. is a member of the scientific advisory board of FL2021-002, a Foresite Labs company, and consults for Interline Therapeutics.

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AlQuraishi, M., Sorger, P.K. Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nat Methods 18, 1169–1180 (2021). https://doi.org/10.1038/s41592-021-01283-4

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