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- AI Drug Discovery Platform Using Multi-Omics Data
AI Drug Discovery Platform Using Multi-Omics Data
Multi-omics AI data platform for drug, indication, target discovery and repurposing
Cost reduction in early-stage drug discovery
Validated candidates with confirmed biological activity
Reduction in unnecessary synthesis of drug candidates
HTG is accelerating precision medicine from diagnosis to treatment by harnessing the power of transcriptome-wide profiling to drive translational research
Check TestimonialBiotech
Industry
USA
Location
AI Ops & MLOps, Cloud Architecture, UI/UX Design
Services
$200,000 to $999,999
Budget
The team surpassed expectations on timelines, provided much needed guidance and overall input on design, all while operating with a high degree of autonomy.
Carl Kaub
Vice President of Chemistry at HTG Molecular DiagnosticEfficient drug and target discovery and repurposing using genomic, transcriptomic, and proteomic data
See what we can do for youSolution
Our team developed a multi-omics AI data platform enabling the generation of drug candidates with desired physicochemical (logP, logS, pKa, BBBP, etc.) and biological (binding affinity, metabolic stability, toxicity, side effects, etc.) properties.
To support high-throughput drug discovery and streamline multi-omics data processing across scientific teams, Blackthorn AI applied a production-grade tech stack including:
Project duration
Team Size
Delivering Impact
Beyond the values already highlighted, there’s even more to discover. Our commitment to innovation, client success, and impactful results sets us apart
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Validated candidates with confirmed biological activityOut of 300 molecules screened, 23 candidates demonstrated IC50 values lower than the reference compound
90%
Reduction in unnecessary synthesis of drug candidatesAdvanced in silico screening and predictive modeling filtered out low-potential molecules prior to synthesis, minimizing chemical resource use and accelerating time-to-lead
80%
Cost reduction in early-stage drug discoveryBy leveraging AI-driven multi-omics data integration and candidate prioritization, the platform significantly reduced experimental overhead and wet-lab costs in the hit-identification phase
