Kim, Philip M.

The Genetic Landscape of a Cell

Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L. Y., Toufighi, K., Mostafavi, S., et al. (2010) · Science

Bioinformatics and Genomic NetworksFungal and yeast genetics research

Cited by 2,162

Paired-End Mapping Reveals Extensive Structural Variation in the Human Genome

Korbel, J. O., Urban, A. E., Affourtit, J. P., Godwin, B., Grubert, F., Simons, J. F., Kim, P. M., Palejev, D., Carriero, N. J., Du, L., et al. (2007) · Science

Genomic variations and chromosomal abnormalitiesGenome Rearrangement Algorithms

Cited by 1,163

The Evolutionary Landscape of Alternative Splicing in Vertebrate Species

Barbosa-Morais, N. L., Irimia, M., Pan, Q., Xiong, H. Y., Gueroussov, S., Lee, L. J., Slobodeniuc, V., Kutter, C., Watt, S., Çolak, R., et al. (2012) · Science

RNA Research and SplicingRNA and protein synthesis mechanisms

Cited by 1,063

Tissue-specific alternative splicing remodels protein-protein interaction networks

Ellis, J. D., Barrios-Rodiles, M., Çolak, R., Irimia, M., Kim, T., Calarco, J. A., Wang, X., Pan, Q., O'Hanlon, D., Kim, P. M., et al. (2012) · Molecular Cell

RNA Research and SplicingCancer-related molecular mechanisms research

Cited by 431

C2H2 zinc finger proteins greatly expand the human regulatory lexicon

Najafabadi, H. S., Mnaimneh, S., Schmitges, F. W., Garton, M., Lam, K. N., Yang, A., Albu, M., Weirauch, M. T., Radovani, E., Kim, P. M., et al. (2015) · Nature Biotechnology

Genomics and Chromatin DynamicsChromosomal and Genetic Variations

Cited by 346

Computational and artificial intelligence-based methods for antibody development

Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences

Monoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approaches

Cited by 178

Large-scale interaction profiling of PDZ domains through proteomic peptide-phage display using human and viral phage peptidomes

Ivarsson, Y., Arnold, R., McLaughlin, M., Nim, S., Joshi, R., Ray, D., Liu, B., Teyra, J., Pawson, T., Moffat, J., et al. (2014) · Proceedings of the National Academy of Sciences

Chemical Synthesis and AnalysisAdvanced Proteomics Techniques and Applications

Cited by 135

PepNN: a deep attention model for the identification of peptide binding sites

Abdin, O., Nim, S., Wen, H., & Kim, P. M. (2022) · Communications Biology

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 104

ProteinSGM: Score-based generative modeling for de novo protein design

Lee, J. S., Kim, J., & Kim, P. M. (2022) · Nature Computational Science

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 94

Score-based generative modeling for de novo protein design

Lee, J. S., Kim, J., & Kim, P. M. (2023) · Nature Computational Science

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 94

A structural approach reveals how neighbouring C2H2 zinc fingers influence DNA binding specificity

Garton, M., Najafabadi, H. S., Schmitges, F. W., Radovani, E., Hughes, T. R., & Kim, P. M. (2015) · Nucleic Acids Research

Protein Structure and DynamicsGenomics and Chromatin Dynamics

Cited by 48

Protein engineering by highly parallel screening of computationally designed variants

Sun, M. G. F., Seo, M.-H., Nim, S., Corbi-Verge, C., & Kim, P. M. (2016) · Science Advances

Protein Structure and DynamicsMonoclonal and Polyclonal Antibodies Research

Cited by 41

Democratizing protein language model training, sharing and collaboration

Su, J., Li, Z., Tao, T., Han, C., He, Y., Dai, F., Yuan, Q., Gao, Y., Si, T., Zhang, X., et al. (2026) · Nature Biotechnology

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 35

PepFlow: direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion

Abdin, O., & Kim, P. M. (2023) · Nature Machine Intelligence

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 28

Gate-based quantum computing for protein design

Khatami, M. H., Mendes, U. C., Wiebe, N., & Kim, P. M. (2023) · PLOS Computational Biology

Quantum Computing Algorithms and ArchitectureDNA and Biological Computing

Cited by 26

Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins

Hao, Y., Colak, R., Teyra, J., Corbi-Verge, C., Ignatchenko, A., Hahne, H., Wilhelm, M., Kuster, B., Braun, P., Kaida, D., et al. (2015) · Cell Reports

RNA Research and SplicingGenomics and Phylogenetic Studies

Cited by 23

FlowPacker: protein side-chain packing with torsional flow matching

Lee, J. S., & Kim, P. M. (2025) · Bioinformatics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 8

Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver

Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., & Kim, P. M. (2021) · STAR Protocols

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 6

EuDockScore: Euclidean graph neural networks for scoring protein–protein interfaces

McFee, M., Kim, J., & Kim, P. M. (2024) · Bioinformatics

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 3