MindWalk Reports Dengue Preclinical Data Validating HYFT Platform Prediction

MindWalk Holdings reported preclinical dengue data that support a computational prediction its HYFT platform generated before any animal was immunized. HYFT computationally identified a candidate pan-serotype structural target on the dengue virus. The subsequent immunization data support that prediction at the antibody-binding level: animals immunized with immunogens designed on the predicted target generated antibodies that cross-reacted with antigens of all four dengue serotypes - a clear and consistent trend, reproduced at the cohort level across two independent campaigns that used distinct immunogen formats and adjuvants. Scrambled-sequence controls did not show the same cross-serotype recognition, indicating the response was specific to the designed immunogen. The result carries implications beyond the dengue program. AI-driven drug discovery depends on biological ground truth - experimentally grounded signal connecting a computational prediction to a measurable outcome in living systems. That substrate has been largely absent from the field: generated inconsistently, outside regulated frameworks, and rarely structured to meet the demands of enterprise AI deployment. The HYFT platform is designed to produce it. Today's data are MindWalk's first public evidence that it can. The data document the platform delivering on its fundamental claim: HYFT technology computationally prioritized the pan-serotype target before immunization, the experiment supported the binding-level prediction, and the experimentally supported target now anchors MindWalk's dengue pipeline IP position as a persistent asset within the company's Biointelligence Platform. The same predictive capability applied in dengue is being deployed across MindWalk's influenza program and other announced programs where multi-strain coverage presents the analogous design challenge. "The central question for any AI drug discovery platform is whether its computational predictions correspond to real biology," said Dr. Jennifer Bath, CEO. "HYFT is built to identify functional patterns that are constrained by biophysical reality - patterns that evolution cannot easily escape because they are tied to structural and binding requirements. We identified the dengue target region not because it appeared conserved at the sequence level, but because HYFT recognized it as functionally constrained in a way that conventional sequence analysis would not prioritize. When animals immunized with immunogens built around that prediction produced antibodies that recognized antigens from all four dengue serotypes - and scrambled-sequence controls did not - the prediction was supported in a live immunological system. That is a closed prediction-to-result loop, and it is the first time we have documented one on this vaccine platform. The antibodies' functional profile - whether they neutralize virus and how they interact with ADE pathways - is what the next body of work will determine. What the current result tells us is that the platform identified real biology. The question now is how broadly that capability holds."