Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

Schissel, C. K.; Mohapatra, S.; Wolfe, J. M.; Fadzen, C. M.; Bellovoda, K.; Wu, C.-L.; Wood, J. A.; Malmberg, A. B.; Loas, A.; Gomez-Bombarelli, R.; Pentelute, B. L.

doi:10.1101/2020.04.10.036566

bioRxiv · 10.1101/2020.04.10.036566

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

Schissel, C. K.·Mohapatra, S.·Wolfe, J. M.·Fadzen, C. M.·Bellovoda, K.·Wu, C.-L.·Wood, J. A.·Malmberg, A. B.·Loas, A.·Gomez-Bombarelli, R.·Pentelute, B. L.

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Abstract

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we couple supervised and unsupervised deep learning with high-throughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequence-activity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases. One sentence summaryDeep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus.

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Schissel, C. K., Mohapatra, S., Wolfe, J. M., Fadzen, C. M., Bellovoda, K., Wu, C.-L., Wood, J. A., Malmberg, A. B., Loas, A., Gomez-Bombarelli, R., Pentelute, B. L.. 2020-04-13. Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers. https://doi.org/10.1101/2020.04.10.036566

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