David L. Mobley
Papers
1
Total Citations
10
H-Index
1
About
David L. Mobley is a leading figure in computational chemistry and biophysics, whose work centers on advancing predictive modeling for biomolecular design. His primary research areas include free energy methods, force field development, and the prediction of protein-ligand binding affinities—critical tools for drug discovery. Mobley is best known for championing community-driven blind prediction challenges, such as the SAMPL series, which rigorously test and improve computational models against experimental data. His landmark 2016 paper, "Advancing Predictive Modeling Through Focused Development Of Model Systems To Drive New Modeling Innovations," with 10 citations, outlines a strategic framework for accelerating model innovation by focusing on well-defined model systems. This work has catalyzed progress in free energy calculations, enabling more accurate predictions of biomolecular interactions. Mobley’s impact extends beyond citations; he has shaped the field by fostering collaboration, transparency, and reproducibility, earning recognition as a thought leader in computational drug design. His contributions continue to inspire researchers seeking to bridge the gap between theory and practical application in molecular modeling.
Research Focus
Key Achievements
Top Papers
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