Scientists use machine learning to develop an opener for a molecular can

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In an era of medical care that is increasingly aiming at more targeted medication therapies, more individual therapies and more effective therapies, doctors and scientists want to be able to introduce molecules to the biological system to undertake specific actions.

Examples are gene therapy and drug delivery, which for widespread use need to be both effective and inexpensive. In service of this goal, a trio of researchers has used machine learning to design a way to remove molecules inside a molecular cage. Their study is published in Physical Review Letters.

The research, whose lead author is Ryan K. Krueger of Harvard University, but to which each co-author contributed equally, uses differentiable molecular dynamics to design complex reactions to direct the system to specific outcomes.

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