Soo-Kyung Kim

Currently, I serve as lead scientist of the Biomacromolecular Modeling Section at the Materials and Process Simulation Center (MSC), Caltech, where I have worked since 2006 under the mentorship of Professor William A. Goddard III. My research has centered on computational modeling of G protein-coupled receptors (GPCRs), one of the largest and most clinically important protein families in drug discovery. I have developed computational workflows that combine homology modeling, ensemble structural prediction, docking, MD, free-energy calculations, and VS to characterize receptor activation mechanisms and design highly selective agonists and antagonists. My work has contributed to numerous GPCR drug discovery programs, including various class A GPCRs, the Urotensin II, serotonin 5-HT2B/2C, β2-adrenergic, chemokine CCR5, adenosine A2A and A3, histamine H3, and olfactory OR1G1 receptor, class B GPCRs, GLP-1, class C GPCRs, sweet (TAS1R2/1R3), class F GPCRs, smoothened, and class T GPCRs, bitter taste (TAS2R) receptors, and More recently, I have expanded these computational approaches to additional therapeutic targets, including the μ-opioid receptor, 5-HT1E receptor, SARS-CoV-2 proteins, and the transcription factor TWIST. Through VS and scaffold optimization, I identified a lead compound from an FDA-approved drug library that predicted to bind the SARS-CoV-2 spike protein with more than 2,000-fold higher affinity than the ACE2 receptor. I have also applied AI-guided computational design to kinase-targeting PROTACs for cancer therapeutics, including CDK6- and FLT3-targeted degraders. My expertise spans ligand-based drug design, structure-based drug design, docking, MD simulations, pharmacophore modeling, ADMET prediction, binding affinity estimation, and large-scale VS.