

What's the Problem?
The development of biomaterials and medications is lengthy and costly, dependent on multiple variables and subject to trial and error. Although artificial intelligence (AI) has accelerated these processes, its effectiveness is limited by high computational costs and the lack of application of first principles physics to understand the molecular properties of materials, thereby prolonging and increasing the cost of drug design. Incomplete molecular understanding has restricted the optimal development of biomaterials, hindering improvement and efficiency in discovery processes. This underscores the need for innovations that integrate fundamental physical principles.


How are they Solving it?
We offer an innovative modeling tool to predict the physical properties of macromolecular suspensions (e.g., proteins, biopolymers, etc.). Our platform, based on the disruptive NESCGLE theory developed by us, is capable of describing the behavior of macromolecular systems under non-equilibrium conditions. By applying it to specific problems, we can drastically reduce the time and costs associated with research and development (R&D) compared to traditional “trial and error” methods. This revolutionary approach aims to transform R&D processes, providing greater efficiency, precision, and predictive capability, thereby fostering new scientific discoveries.