The Role of Multiscale Modeling in Molecular Discovery

Advanced statistical methods are rapidly impregnating many scientific fields, offering new perspectives on long-standing problems. In material science, data-driven methods are already bearing fruit in various disciplines, such as hard condensed matter or inorganic chemistry, while comparatively little has happened in soft matter. I will describe how we use multiscale simulations to leverage data-driven methods in soft matter. We aim at establishing structure-property relationships for complex thermodynamic processes across the chemical space of small molecules. Akin to screening experiments, we devise a high-throughput coarse-grained simulation framework. Coarse-graining is an appealing screening strategy for two main reasons: it significantly reduces the size of chemical space and it can suggest a low-dimensional representation of the structure-property relationship. I will briefly mention a biological application of our methodology that led to the discovery of in vitro active compounds. Finaly, I will mention a number of ways machine learning can help fulfill the promise of connecting models at different scales.