Tokyo-based Elix and the University of Vienna signed a joint research agreement aimed at advancing drug discovery using AI technologies.
One of Elix’s business models is the provision of its integrated AI drug discovery platform, Elix Discovery™, developed under the concept of “medicinal chemists can truly use it.” The product comes with an intuitive graphical user interface (GUI) that automatically constructs predictive models for optimal compound profiling. Elix says it also features diverse structure generation capabilities, with a strength in “proposing structures that humans would not conceive.”
By integrating curated structure generation models, including proprietary ones, with predictive models and parameters built into the intuitive GUI, researchers can rapidly and intuitively refine molecular design, notes a company spokesperson, adding that platform supports both ligand-based drug design (LBDD), including pharmacophore modeling and structure-based drug design (SBDD), the latter utilizing docking simulations and other methods, thus enabling exploration across a broader range of approaches.
The second business model focuses on collaborative drug discovery research with pharmaceutical companies, biotech ventures, and academia.
Led by Julien Orts, MSc, PhD, associate professor at the University of Vienna, this research group specializes in NMR spectroscopy techniques to decode the atomic-resolution structures, dynamics, and interactions of biomacromolecules. The lab’s primary focus involves studying protein conformational switches and allostery in signaling, utilizing groundbreaking methodologies they developed such as INPHARMA for validating small-molecule binding modes and NMR for the automated determination of protein-ligand structures.
INPHARMA is a European research and training network focused on improving drug formulation processes, enhancing patient safety, and reducing animal testing in pharmaceutical development.
By applying exact nuclear Overhauser enhancement (eNOE) distance measurements with 0.1 Å accuracy to resolve protein ensembles, scientists in the Orts Lab note that they provide the thermodynamic insights necessary to tackle undruggable targets and advance modern structure-based drug design.
In this joint research project, Elix will collaborate with the Orts research. The parties see this arrangement as merging Elix’s proprietary expertise in chemoinformatics-based approaches and AI-driven molecular generation with the Orts group’s capabilities in structural dynamics and NMR-validated molecular interactions.
The collaboration aims to design and develop novel compounds against traditionally undruggable targets, with a specific focus on intrinsically disordered proteins (IDPs) and proteins implicated in epigenetic signaling and cancer. By integrating AI’s predictive power with experimental atomic-resolution data, the project seeks to bypass the limitations of traditional drug discovery, delivering new therapeutic insights and next-generation candidates for complex diseases, according to the researchers.
We are thrilled to partner with Elix to bridge the gap between advanced structural biology and artificial intelligence. My laboratory has always been driven by the desire to push the boundaries of what NMR can achieve in drug discovery,” says Orts. “By combining our ability to resolve protein dynamics at atomic precision with Elix’s sophisticated AI-driven generation, we can move beyond static structures and begin to target the complex, transient behaviors of proteins that were once considered out of reach. This synergy is exactly what is needed to accelerate the discovery of transformative medicines for the next generation.”
“At Elix, our mission is to rethink drug discovery by bridging cutting-edge AI with experimental innovation,” points out Shinya Yuki, PhD, CEO at Elix. “Collaborating with the [Orts] group allows us to pursue this mission on a global scale, uniting expertise in AI drug discovery with structural biology. We believe this partnership will open new possibilities for targeting diseases that have long remained beyond the reach of traditional drug discovery.”
