New grants for Masaar Dynamics, MoleSense, Raven Robotics and Recarta

Masaar Dynamics

The team behind Masaar Dynamics have developed software to help scientists and pharmaceutical companies understand and develop protein-based therapies.

When it comes to developing drugs, the two most established categories are small molecule and biologics. Small molecule drugs (such as aspirin or penicillin) tend to be chemical based and can take many forms. Biologics (drugs such as insulin or certain cancer drugs) are made from proteins. This means they are much larger and more complex than small molecules. During the drug discovery process, scientists often use computer simulations to predict how drugs will interact with their targets. While these models are well-established for small molecule drugs, there’s a significant gap when it comes to biologics. Without effective simulation tools, researchers rely on lab experiments which are costly, slow, and can be inaccurate.

The Masaar Dynamics team is using AI and digital simulation to create a platform where scientists can understand the behaviour and interactions between biomolecules (such as proteins and potential drugs) using computer models. They believe their platform will cut the cost and time needed to validate drug design, making it easier and quicker to bring new treatments to patients.

They will use their grant to test and validate their technology at the world-class EPFL facilities.

Contact:

Moustafa Houmani
EPFL Laboratory for Biomolecular Modeling (LBM)

MoleSense

Led by EPFL researchers from the EPFL Bio/CMOS Interfaces Laboratory, MoleSense aims to advance current wearable technologies by offering molecular insights for personalized, real-time health monitoring.

The team is tackling critical challenges of our modern and developed society. MoleSense wants to bring digital health to maternity care. Their product is poised to offer continuous monitoring of key maternity biomarkers and assist expected mothers in their pregnancy journeys, reducing maternity complications through predictive, preventive, and early diagnostic measures.

With the ignition grant, MoleSense will focus on the next stages of their product development, with plans to roll out a fully functional prototype by 2025.

Contact:

Dr. Gian Luca Barbruni, Postdoctoral Researcher
Bio/CMOS Interfaces (BCI) Laboratory

Raven Robotics

Raven Robotics aims to enhance Health, Safety, and Environment (HSE) inspections across various industrial settings, including construction sites, power plants, and manufacturing facilities, through persistent monitoring utilizing autonomous drones with self-charging and hazard detection capabilities.

These workplaces are typically large, congested, and dynamic, presenting substantial HSE challenges, such as workplace accidents, operational inefficiencies, and environmental issues. Traditional inspection methods, involving manual inspections and remotely operated robotic systems, face limitations such as high costs, operational downtime, and the requirement for specialized personnel, which diminish the frequency and quality of inspections. Additionally, the use of stationary cameras poses deployment challenges and lacks versatility in terms of the spectrum and quality of data captured. Finally, scaling these inspections as the areas expand poses additional difficulties.

Raven Robotics addresses these challenges by offering a cost-effective and efficient solution: autonomous Micro Aerial Vehicles (MAVs). These very compact drones, equipped with autonomous navigation, hazard detection, and self-charging capabilities, can conduct continuous inspections with minimal human intervention. Their versatility, demonstrated by their ability to operate both indoors and outdoors, makes them particularly useful in large sites with diverse environments. This adaptability ensures that no area is left uninspected, enhancing the overall safety and efficiency of the site. Crucially, these drones can also collaborate, performing tasks collectively and enhancing scalability.

Their Ignition grant will be used to refine their design and create the first version of their prototype.

Contact:

Izzet Kagan Erünsal, Doctoral Assistant
EPFL Distributed Intelligent Systems and Algorithms Laboratory (DISAL)

Recarta

Buildings in Switzerland currently use around 40% of the country’s energy supply. They are also responsible for around one third of Switzerland’s CO2 emissions. To combat this, a new energy policy – Energy Strategy 2050 – has been put in place. It aims to reduce the energy consumption of Swiss buildings by nearly 30% by 2050. This new target creates both challenges and opportunities for companies who manage real estate portfolios. Although there will be the short-term costs of renovation and compliance, they would be counterbalanced by lower operating costs and increased property values in the long-term. If the strategy is to succeed, however, incentives will need to be built into contracts and there will need to be constant monitoring of progress.

The team at Recarta have identified a number of gaps in the current way in which real estate managers keep track of their portfolio – too often relying on basic spreadsheets. In response, they have developed a new AI-based large language model (LLM) technology to streamline and improve the management of large real estate investment portfolios. Their technology automatically processes contracts, measures performance (monitoring gaps), and helps identify areas of opportunity or impact.

The team will use their grant to gather further data and market insights to test and validate their technology.

Contacts:

Géraud de Laval, Co-founder and Chief Impact Officer
Etienne Friedli, Co-Founder & CEO