DDF Summit 2024

CalendarDate & Time
  • May 21st-23rd, 2024
  • Berlin, Germany

Schrödinger is excited to be participating in the DDF Summit taking place on May 21st – 23rd in Berlin, Germany. Join us for a presentation by John Shelley, Fellow at Schrödinger, titled “Molecular Modeling and Machine Learning for Small Molecule and Biologic Drug Formulation.”


John Shelley


John earned a MSc from the University of Waterloo in theoretical chemistry and a PhD from the University of Pennsylvania in computational chemistry.  Following post-doctoral research in computational chemistry at the University of British Columbia, he worked for Procter & Gamble studying surfactant structures in solution.  For the last 23 years, John has worked for Schrödinger, LLC, as a scientific software developer and a research scientist, managing a number of products including the Materials Science Coarse-Grained product.  John has focused on computer modeling of drug formulations for much of the last 8 years.


Selecting and combining the right ingredients in the appropriate manner is essential for successful drug formulation given the inherent challenges and competitive market. With advances in modern machine learning, physics-based simulation techniques and computer hardware, modelling is emerging as a valuable source of information that complements experimental characterization.  We showcase a cross-section of capabilities within Schrödinger’s Suite for modeling related to formulations of small-molecule or biologic drugs.
For small-molecule drugs workflows have been created for characterizing crystal polymorphs, crystal morphology and degradation risks as well as calculating elastic constants (bulk modulus, shear modulus, etc.), powder diffraction patterns, glass transition temperatures (Tg), diffusion constants, pKa values, melting points, water adsorption and various solubilities. For biologics our toolset supports homology modeling, and the calculation of aggregation propensity, titration curves, isoelectric points and viscosity among other things.
Complex and evolving structures, often in fluid states, play a crucial role in the pharmaceutical industry.   For both small-molecule and biologics formulations powerful simulation tools employing atomistic or coarse-grained models to permit the characterization of molecular interactions and nanoscale structuring, sometimes within otherwise disordered bulk systems (e.g., LNP formation, self-assembly of polymer-based structures, dissolving amorphous solid dispersions, liposomes and protein-excipient interactions).

Key Learning Objectives:

  • Advances in crystal structure prediction
  • API and excipient physical and chemical property prediction from molecular modeling and machine learning
  • Molecular modeling for lipid nanoparticles
  • Molecular modelling provides data and a basic understanding of the behaviour of drug formulations that compliments experimental data and machine learning to inform decision making