BELMONT — Scientists at the Belmont Institute for Computational Biology have published findings that could significantly accelerate pharmaceutical drug discovery, describing a new algorithm that reduces the time required for protein-folding simulation by approximately 40 percent.

The research, published Thursday in the journal Structural Computation Review, describes a technique the team calls iterative lattice compression — a method of approximating the three-dimensional folding behavior of peptide chains without running a full atomic-level simulation for each iteration. By pre-screening candidate structures using a genome-indexed reference library, the algorithm eliminates roughly two-fifths of the conventional processing steps before a high-fidelity simulation is even initiated.

“The latency problem in drug discovery isn’t the chemistry,” said Dr. Yuna Harada, the institute’s lead researcher and the paper’s primary author. “It’s the computational cost of modeling how a candidate molecule will actually behave inside a living system. We’ve found a shortcut that doesn’t sacrifice meaningful accuracy.”

The technique is particularly relevant for catalyst design — identifying molecular compounds that can trigger specific biological reactions without becoming consumed in the process. Harada’s team tested the algorithm against a library of 11,000 known peptide structures and found that its predictions matched peer-reviewed experimental results in 94 percent of cases, a figure the team acknowledges still leaves room for refinement.

Not everyone in the field is convinced the shortcut holds up at scale. Prof. Sven Almquist of the Northern Institute of Structural Biology called the results “promising but incomplete.” “Iterative compression works well when the reference library is dense,” Almquist wrote in a response published alongside the study. “The question is whether it degrades gracefully when you’re modeling novel folding topologies that aren’t well-represented in any genome-indexed set. That’s where the real world tends to surprise you.”

Harada said the team plans a follow-up study addressing exactly that concern, using synthetically generated peptide sequences that deliberately fall outside common reference clusters.

The practical implications, if the method scales, are significant. Pharmaceutical companies routinely spend years and substantial capital on early-stage simulation work before a candidate compound reaches animal testing. A 40 percent reduction in simulation latency could meaningfully compress that timeline and reduce the cost of exploring larger candidate libraries.

The Belmont Institute has filed a provisional patent on the lattice compression technique. Harada said the team intends to publish the core algorithm as open-source software within the next six months.