Thermodynamic and Electrostatic Methods for Modeling Large Organic Crystals

About This Grant

Michael Schnieders of the University of Iowa is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop new computational methods to simulate and predict the physical properties of complex organic crystals. These crystals play a vital role in daily life: from helping to transform promising pharmacueticals into bioavailable tablets to allowing scientists to explore how the building blocks of cells function using crystallography experiments. For small molecules with just a few dozen atoms, computational methods can predict packing into different crystalline structures called polymorphs—each with unique properties such as stability and water solubility. But for larger, more flexible molecules—like proteins or new drugs with hundreds or thousands of atoms—this prediction becomes far more difficult. Schnieders and his team will tackle this challenge in two key ways. First, they’ll create a method—up to 100 times faster—to determine which crystal polymorph is most stable by simulating how crystals shift from one form to another. Second, they’ll integrate acid/base chemistry into their models to better capture how molecular crystals are influenced by pH. These breakthroughs could speed up the design of new therapuetics and help uncover enzymatic mechanisms. Beyond the lab, Schnieders will spark interest in science through a high school internship program, train graduate students in cutting-edge computational skills, and share the software developed to support these efforts , Force Field X, freely with the scientific community. Organic crystals are essential for developing bioavailable pharmaceuticals and studying biomolecular structures via crystallography. Molecules within a crystal can form multiple polymorphs, each with distinct properties like stability and solubility, making accurate prediction of observed polymorphs critical. Current methods for estimating free energy differences between polymorphs rely on inefficient approaches, such as the Einstein crystal method, which involves summing large, opposing free energy terms. Schnieders will develop methods to overcome this by introducing a novel dual-topology approach for solid-solid phase transitions, enabling direct, efficient estimation of relative free energies between complex crystal polymorphs with equilibrium sampling methods. For protein crystals, Schnieders will advance molecular simulations by integrating charge penetration, charge transfer, and neural network terms into polarizable atomic multipole models, enhancing the treatment of (de)protonation via constant pH molecular dynamics (CpHMD). These innovations will improve the accuracy of crystal structure prediction and deepen insights into protein behavior. Broader impacts include mentoring high school students in STEM, integrating new methods into a Computational Biochemistry course for graduate training, and disseminating all algorithms via the open-source Force Field X software (http://ffx.biochem.uiowa.edu). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.