Announcement • Jun 02

MindWalk's Preclinical Dengue Data Support a HYFT® Computational Prediction: Immunogens Designed on the Predicted Target Elicited Cross-Reactive Antibodies Against Antigens of All Four Dengue Serotypes, Reproduced Across Two Independent Campaigns

MindWalk Holdings Corp. 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. The HYFT platform is designed to produce it. The platform delivered 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. Dengue infects an estimated 400 million people annually across four antigenically distinct serotypes. Approved dengue vaccines reach only previously seropositive populations because cross-reactive antibodies against one serotype can enhance, rather than neutralize, disease upon first exposure to a second — antibody-dependent enhancement (ADE). The unsolved pan-serotype problem has kept a significant portion of the endemic seronegative population outside the addressable market for dengue vaccination, and has made a validated pan-serotype immunogen IP position the critical upstream asset for every next-generation dengue vaccine, broad dengue antibody, and pan-flaviviral platform program in development. Identifying that position requires operating below the level of sequence homology. Serotype-to-serotype sequence identity across dengue envelope domain III (EDIII) ranges from 74% to 83% — high enough to generate broadly cross-reactive antibodies, insufficient to distinguish a functionally constrained pan-serotype target from a cross-reactive decoy. HYFT is designed to make that distinction. Rather than relying on sequence alignment or predicted immunogenicity — the parameters conventional target selection leans on — HYFT identifies functional patterns shaped by biophysical constraints: structure, charge distribution, and binding geometry that remain conserved despite evolutionary diversification because biology cannot easily abandon them. Within MindWalk's Biointelligence Platform, these HYFT patterns function as biological anchor points that connect sequence information to structural context, literature-supported biology, and experimental evidence. That connection is made through LensAI, MindWalk's contextualization and reasoning layer, and the Company's Knowledge Graph — a persistent biological intelligence layer that accumulates and links evidence across programs. The significance of the current dengue data is therefore broader than a single dengue vaccine-design experiment: it provides initial experimental evidence for a repeatable discovery loop in which computationally prioritized biological patterns are tested in vivo, and the resulting evidence can be reused across future discovery programs. The current data support cross-serotype antibody recognition at the binding level; future studies will determine whether these antibodies neutralize virus and assess their relationship to antibody-dependent enhancement. Animals immunized with the immunogens designed around the HYFT-identified target generated antibody responses that recognized antigens from all four dengue serotypes, while scrambled-control immunizations did not show the same cross-serotype recognition profile. HYFT computationally prioritized the target region before immunization, and the subsequent antibody-binding data are consistent with that prediction. For MindWalk, the important result is not only the observed binding response, but also the demonstration that computational biological anchors can be connected to structural analysis, immunogen design, experimental readouts, and a reusable evidence layer. MindWalk's Biointelligence Platform is built around three complementary components. HYFT identifies biologically meaningful anchor patterns across sequence and structural space. The Knowledge Graph connects those patterns to structural, functional, literature, and experimental evidence. LensAI provides the contextualization and reasoning layer that enables scientists and AI models to operate over a common biological representation. Together, these components are designed to create a reusable biological intelligence infrastructure rather than a workflow optimized for any single disease program. MindWalk's Knowledge Graph currently contains hundreds of millions of biological patterns and tens of billions of biological relationships accumulated through years of biological data integration and enrichment. Each experimentally supported result contributes additional evidence to this growing representation, strengthening its potential value across future programs in infectious disease, immunology, protein engineering, and therapeutic discovery. The dengue study also demonstrates the platform's ability to identify both broadly conserved and biologically differentiated patterns within the same analytical framework — a capability particularly relevant to discovery programs where balancing broad coverage and selective targeting represents a central design challenge.

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MindWalk's Preclinical Dengue Data Support a HYFT® Computational Prediction: Immunogens Designed on the Predicted Target Elicited Cross-Reactive Antibodies Against Antigens of All Four Dengue Serotypes, Reproduced Across Two Independent Campaigns

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