• Highlights

    Smart Matter Design

    Understanding light-matter interactions

    The dynamical mechanisms involved in light-matter interaction in materials, dominated by energetically excited quantum states, are notably complex. These complexities present a significant challenge for traditional scientific approaches. To address this, materials science groups at the Weizmann Institute are working on establishing a new theoretical learning algorithm. They are applying AI-based and machine learning approaches to evaluate and understand the outcomes of these intricate particle-particle interactions. This strategy will not only provide insights into the quantum behavior of materials when exposed to sunlight but also offers valuable information for optimizing material structures to enhance these interactions.

  • Highlights

    Particles of Life

    Accelerating antibody development

    Over the past three decades, synthetic antibody repertoires have advanced significantly, aiding the development of novel therapeutic and diagnostic antibodies. Currently, at least six antibodies derived from such repertoires are in clinical use, with hundreds more in clinical trials. Most repertoires contain billions of unique antibodies, generated by recombining fragments of known human antibodies. However, these repertoires often yield antibodies with low stability and a high tendency for polyreactivity (tendency to bind to unwanted targets), complicating the costly development process.

  • Highlights

    Networks of Thoughts

    The design principles of neural architectures

    The functions that a network of neurons may perform is shaped by the map of connections between the neurons. Understanding the principles that govern the architecture of these networks of connections, known as "connectomes," is crucial for advancing our knowledge of brain development and architecture, how learning occurs (when these connections change), and the behavior these networks direct. Researchers at Elad Schneidman’s group are developing “generative models” for the connectomes of networks in the brains of different organisms: the olfactory bulb of zebrafish, the visual cortex of mice, and the complete neural network of the C. elegans worm.

  • Highlights

    Predicting Sustainable Future

    Deciphering dust storms

    Dust storms can pose significant health and ecological implications. Furthermore, dust clouds have a profound effect on climate. They absorb and redistribute sunlight, altering Earth's temperature and influencing the properties of clouds and rainfall patterns. An early warning system for dust storms could potentially protect vulnerable populations and prevent adverse effects on crops. Nonetheless, predicting these storms proves difficult due to their rapid development, what do you mean, and unpredictable intensity and location.

  • Highlights

    Enhancing Clinical Care

    Predicting response to immunotherapies

    Immune-checkpoint blockade therapies have revolutionized the treatment of metastatic melanoma in recent years. However, these therapies do not provide a long-lasting response for many patients. Indeed, 50% of patients succumb to metastatic disease within five years of diagnosis, with responses to immunotherapies even varying between different metastases within a single patient. The tumor microenvironment, specifically immune cells within it, plays a pivotal role in shaping tumor progression. Despite this, we still lack biomarkers to guide therapy and predict response to specific immunotherapies.