Dr. Michael Bellucci is a Senior Director at XtalPi, Inc. where he leads a research team that develops algorithm technologies for solid form development and risk assessment in drug discovery. Prior to his current role, he was research scientist at the Novartis-MIT Center for Continuous Manufacturing. His solid-state research has focused on combining computational chemistry and A.I./machine learning methods to study processes and properties associated with molecular crystals. Michael is a theoretical and computational chemist by education and holds a Ph.D. in chemistry from Boston University.

Solid form screening and selection is a critical process in the formulation development and clinical success of a drug. However, solid form selection can be a tedious, months-long process that carries the intrinsic risk of “missing polymorphs.” Crystal Structure Prediction (CSP) aims to reduce this risk and improve the efficiency of pharmaceutical manufacturing by correctly predicting the low-energy crystal forms of organic molecules from minimal information, such as the chemical diagram. At XtalPi, we have developed a cloud-based computational CSP platform that combines advanced physics-based algorithms with machine learning. Our CSP platform can rapidly generate and rank millions of crystal structures for pharmaceutically relevant systems, as well as accurately predict or explore polymorphism in organic crystal compounds to provide indispensable insight into the properties of molecular crystals for an accelerated solid form selection process.