David C. Wright
Chief Scientific Officer LPJP, Inc.
In 2009, Dr. David Craig Wright came out of retirement after over 30 years of experience seeing patients and working in the biopharmaceutical industry to open a private practice in Carmel, California. He moved his practice to Monterey, CA and opened up the infusion center in 2010. In 2017, he moved his practice and infusion center to Pacific Grove, CA. In 2020, Dr. Wright cofounded D4 Labs, a research lab working on adjuvants and vaccines. Board Certified in Internal Medicine and Infectious Disease, Dr. Wright holds 15 patents and is a graduate of the College of Arts and Sciences and the Medical School at the University of Virginia in Charlottesville, Va. He completed his internship at Columbia Presbyterian/Harlem Hospital in New York and his residency in Internal Medicine and Infectious Disease and Tropical Medicine training at Walter Reed Army Medical Center in Washington, D.C.
Seminars
Accurately predicting how molecules bind to proteins remains one of the major unsolved challenges in structure-based drug design, with current approaches struggling to rank binding affinities, predict accurate poses and design proteins with sufficiently high success rates. This technical workshop will explore how AI, physics-based modeling and experimental validation can be combined to improve predictive performance by:
- Improving small-molecule docking and binding pose prediction by examining the limitations of current approaches, including challenges with homologous versus novel structures and the difficulty of accurately modeling protein-ligand interactions
- Advancing affinity prediction and free-energy calculations to better rank compounds by exploring how AI and physics-based approaches can address complex parameters such as protein flexibility, solvent and water interactions that limit current predictive accuracy
- Strengthening de novo protein design and computationally designed binders by examining how prediction models can be improved, experimentally validated and coupled with high-throughput screening to increase the success rate of designed proteins
- Benchmarking predictive models against experimental data to identify which AI/ML approaches genuinely improve SBDD outcomes and where expert oversight and wet-lab validation remain essential
