Generative AI and molecular modeling for de novo compound design, lead optimization, and structure-activity prediction.
Multi-omics data integration and machine learning pipelines to identify and validate novel therapeutic targets.
In silico screening of existing compounds to uncover new indications and predict clinical success probability.
Tailored AI models, dataset curation, and simulation workflows for academic or pharmaceutical research needs.
Founded by a collective of researchers, clinicians, and engineers united by a shared vision to redefine drug discovery through intelligent computation and scientific collaboration.
The chemical universe encompasses an almost infinite number of potential compounds — an estimated 10⁶⁰ possibilities, each holding untapped therapeutic promise. Hidden within this vast space may lie cures for diseases still considered incurable. Yet, traditional discovery remains slow, costly, and limited by linear, trial-and-error approaches. Even current AI strategies often focus too narrowly on single targets.
Our platform adapts the therapeutic modality to the nature of the target itself — selecting the most effective strategy for each biological context. Depending on the target, this may involve small molecules, antibodies, peptides, nucleic acid–based drugs (siRNA, mRNA, antisense oligonucleotides), biologics, protein degraders (PROTACs), cell therapies, or gene-editing systems such as CRISPR. This flexible design allows us to address complexity where traditional methods reach their limits.
We combine the power of artificial intelligence, advanced molecular modeling, and cross-disciplinary expertise to accelerate the discovery of new treatments for currently untreatable diseases. Our access to extensive clinical datasets, including patient parameters and therapeutic outcomes, together with fundamental omics and preclinical data, enables us to bridge the gap between research and real-world medicine. A diverse team of scientists, clinicians, and IT specialists works collaboratively using some of the most powerful supercomputers on the planet. We focus on practical implementation, cost efficiency, and measurable results, always guided by one purpose: improving human health through intelligent innovation.
Our approach begins with generative AI capable of creating new molecular and biological entities rather than merely analyzing existing ones. By learning from vast chemical, clinical, and omics datasets, the system can propose novel compounds, mechanisms, and therapeutic hypotheses that follow biologically meaningful patterns.
Drawing inspiration from how the human brain organizes and processes complex information, we apply Hierarchical Feature Binding to teach our AI to interpret biology in contextual, layered relationships. This allows for a more realistic simulation of molecular interactions and disease networks.
Building on these foundations, our Unimolecular Polypharmacy strategy generates compounds designed to act on multiple interconnected targets within a disease pathway. This multi-target approach enhances therapeutic potential, reduces development risk, and paves the way for more effective treatments of complex disorders.
