Researchers used machine learning to optimize the process by which a tiny cage is opened to release a molecule.
Researchers have designed a tiny structure that could help deliver drugs inside the body [1]. The theoretical and computational work required machine learning to optimize the parameters for the structure, which could stick to a closed shell containing a small molecule and cause the shell to open. The results demonstrate the potential for machine learning to assist in the development of artificial systems that can perform complex biomolecular processes.
Researchers are developing artificial molecular-scale structures that could perform functions such as drug delivery or gene editing. Creating such artificial systems, however, usually entails a frustrating tradeoff. If the components are simple enough to be computationally tractable, they are unlikely to yield complex interactions. But if the components are too complex, they become harder to combine and coordinate. Machine learning can reduce the computational cost of designing useful artificial systems, according to graduate student Ryan Krueger of Harvard University.
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