MIRTH harmonizes metabolite datasets and uses non-negative matrix factorization to infer missing measurements without additional experiments.
An MPNN mutates solvent-exposed and loop regions while masking conserved sites, then molecular dynamics ranks thermostable candidates.
Distance map crops model residue relationships efficiently, addressing accuracy and efficiency limits in machine-learning protein structure prediction.
Pretrained protein language models are fine-tuned to generate diverse antimicrobial peptides, shortening discovery and reducing manual screening effort.
Serial alignment, sorting, and deduplication slow genomic resequencing; FPGA parallelism cuts running time and calculation cost.
Protein-level recursive transformers iteratively assemble complex representations to support efficient biologic, peptide, and small-molecule drug design.