RNA design is central to next-generation therapeutics, yet identifying sequences that reliably fold into desired structures remains a major computational challenge, often constrained by high cost and time. A new study from Keio University explores the use of factorization machine with quadratic-optimization annealing (FMQA) for RNA inverse folding, while also examining how different encoding strategies may influence artificial intelligence (AI)-driven design performance, revealing an underexplored dimension of biomolecular engineering.

A new study published in the Proceedings of the National Academy of Sciences (PNAS) finds that individual human neurons are capable of significantly more complex computations than those of other mammals, offering a fresh view to what makes the human brain unique.