| Fast and flexible protein design using deep graph neural networks A Strokach, D Becerra, C Corbi-Verge, A Perez-Riba, PM Kim Cell systems 11 (4), 402-411. e4, 2020 | 258 | 2020 |
| Deep generative modeling for protein design A Strokach, PM Kim Current opinion in structural biology 72, 226-236, 2022 | 95 | 2022 |
| ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity DK Witvliet, A Strokach, AF Giraldo-Forero, J Teyra, R Colak, PM Kim Bioinformatics 32 (10), 1589-1591, 2016 | 84 | 2016 |
| A universal deep-learning model for zinc finger design enables transcription factor reprogramming DM Ichikawa, O Abdin, N Alerasool, M Kogenaru, AL Mueller, H Wen, ... Nature Biotechnology 41 (8), 1117-1129, 2023 | 45 | 2023 |
| Predicting changes in protein stability caused by mutation using sequence‐and structure‐based methods in a CAGI5 blind challenge A Strokach, C Corbi‐Verge, PM Kim Human mutation 40 (9), 1414-1423, 2019 | 37 | 2019 |
| ELASPIC2 (EL2): combining contextualized language models and graph neural networks to predict effects of mutations A Strokach, TY Lu, PM Kim Journal of molecular biology 433 (11), 166810, 2021 | 35 | 2021 |
| Predicting the effect of mutations on protein folding and protein-protein interactions A Strokach, C Corbi-Verge, J Teyra, PM Kim Computational methods in protein evolution, 1-17, 2019 | 24 | 2019 |
| Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge C Savojardo, M Petrosino, G Babbi, S Bovo, C Corbi‐Verge, R Casadio, ... Human mutation 40 (9), 1392-1399, 2019 | 19 | 2019 |
| CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods S Jain, C Bakolitsa, SE Brenner, P Radivojac, J Moult, S Repo, ... Genome Biology 25 (1), 2024 | 5 | 2024 |
| Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver A Strokach, D Becerra, C Corbi-Verge, A Perez-Riba, PM Kim STAR protocols 2 (2), 100505, 2021 | 5 | 2021 |
| Assessing predictions on fitness effects of missense variants in HMBS in CAGI6 J Zhang, L Kinch, P Katsonis, O Lichtarge, M Jagota, YS Song, Y Sun, ... Human genetics, 1-17, 2024 | | 2024 |
| Machine learning approaches for structure-guided protein design A Strokach University of Toronto (Canada), 2024 | | 2024 |
| System and method for generating a protein sequence AV STROKACH, DB ROMERO, CC VERGE, AP Riba US Patent App. 17/781,805, 2022 | | 2022 |
| PROBING THE OLIGOMERIC STATUS OF G PROTEIN-COUPLED RECEPTORS BY SINGLE-MOLECULE FLUORESCENCE AV Strokach, JW Wells JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION 33 (3), 203-203, 2013 | | 2013 |
| Probing the Oligomeric Status of G Protein-Coupled Receptors by Forster Resonance Energy Transfer and Single-Particle Fluorescence A Strokach | | 2013 |
Philip M. KimProfessor, University of Toronto在 kimlab.org 的电子邮件经过验证
