John Ingraham

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Debora MarksSystems Biology, Harvard Medical School, Broad Institute of Harvard and MITVerified email at hms.harvard.edu
Chris SanderHarvard Medical School, Broad Institute, Dana-Farber Cancer Institute, Memorial Sloan KetteringVerified email at sanderlab.org
Adam RiesselmanHippo HarvestVerified email at hippoharvest.com
Thomas A. HopfScientific ConsultantVerified email at thomashopf.com
Michael Springerharvard medical schoolVerified email at hms.harvard.edu
Charlotta ScharfePrincipal Data Scientist, F. Hoffmann-La Roche, SwitzerlandVerified email at roche.com
Frank PoelwijkGenerate Biomedicines LLCVerified email at generatebiomedicines.com
Agnes Toth-PetroczyMPI of Molecular Cell Biology and Genetics and Center for Systems Biology DresdenVerified email at mpi-cbg.de
Perry PalmedoHarvard UniversityVerified email at fas.harvard.edu
Vikas GargMassachusetts Institute of Technology (MIT)Verified email at mit.edu
Tommi JaakkolaMITVerified email at csail.mit.edu
Regina BarzilayProfessor of EECS, MITVerified email at csail.mit.edu
Dieter ArmbrusterProfessor of Mathematics, Arizona State UniversityVerified email at asu.edu
Benjamin SchubertGroup Leader, ICB@Helmholtz Center MunichVerified email at helmholtz-muenchen.de
Anna G. GreenAssistant Professor, Manning College of Information and Computer Sciences, UMass, AmherstVerified email at umass.edu
Christian DallagoNVIDIA & Technische Universität MünchenVerified email at tum.de
Kelly Paige BrockKernal BiologicsVerified email at kernalbio.com
Jue WangUniversity of WashingtonVerified email at uw.edu
Renan Escalante-ChongLoxo Oncology at Lilly

John Ingraham

Generate Biomedicines, Inc

Verified email at csail.mit.edu - Homepage

Machine Learning Computational Biology Protein Design


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Mutation effects predicted from sequence co-variation TA Hopf, JB Ingraham, FJ Poelwijk, CPI Schärfe, M Springer, C Sander, ... Nature Biotechnology 35 (2), 128-135, 2017	669	2017
Deep generative models of genetic variation capture the effects of mutations AJ Riesselman, JB Ingraham, DS Marks Nature Methods 15, 816-822, 2018	601	2018
Generative Models for Graph-Based Protein Design J Ingraham, VK Garg, R Barzilay, T Jaakkola Neural Information Processing Systems, 2019	514	2019
Defining variant-resistant epitopes targeted by SARS-CoV-2 antibodies: A global consortium study KM Hastie, H Li, D Bedinger, SL Schendel, SM Dennison, K Li, ... Science 374 (6566), 472-478, 2021	281	2021
Basketball teams as strategic networks JH Fewell, D Armbruster, J Ingraham, A Petersen, JS Waters PloS one 7 (11), e47445, 2012	259	2012
Illuminating protein space with a programmable generative model JB Ingraham, M Baranov, Z Costello, KW Barber, W Wang, A Ismail, ... Nature 623 (7989), 1070-1078, 2023	258	2023
The EVcouplings Python framework for coevolutionary sequence analysis TA Hopf, AG Green, B Schubert, S Mersmann, CPI Schärfe, JB Ingraham, ... Bioinformatics 35 (9), 1582-1584, 2019	226	2019
3D RNA and Functional Interactions from Evolutionary Couplings C Weinreb, AJ Riesselman, JB Ingraham, T Gross, C Sander, DS Marks Cell 165 (4), 963-975, 2016	193	2016
Learning Protein Structure with a Differentiable Simulator J Ingraham, A Riesselman, C Sander, D Marks International Conference on Learning Representations, 2019	165	2019
Structured States of Disordered Proteins from Genomic Sequences A Toth-Petroczy, P Palmedo, J Ingraham, TA Hopf, B Berger, C Sander, ... Cell 167 (1), 158-170. e12, 2016	151	2016
Galactose metabolic genes in yeast respond to a ratio of galactose and glucose R Escalante-Chong, Y Savir, SM Carroll, JB Ingraham, J Wang, CJ Marx, ... Proceedings of the National Academy of Sciences 112 (5), 1636-1641, 2015	141	2015
Cellbox: Interpretable machine learning for perturbation biology with application to the design of cancer combination therapy B Yuan, C Shen, A Luna, A Korkut, DS Marks, J Ingraham, C Sander Cell systems 12 (2), 128-140. e4, 2021	115	2021
Generating transition states of isomerization reactions with deep learning L Pattanaik, JB Ingraham, CA Grambow, WH Green Physical Chemistry Chemical Physics 22 (41), 23618-23626, 2020	54	2020
Variational Inference for Sparse and Undirected Models J Ingraham, D Marks International Conference on Machine Learning, 2017	44*	2017
Simultaneous enhancement of multiple functional properties using evolution-informed protein design B Fram, Y Su, I Truebridge, AJ Riesselman, JB Ingraham, A Passera, ... Nature Communications 15 (1), 5141, 2024	6	2024
Antigen Binding Molecules Targeting SARS-CoV-2 G Grigoryan, J Ingraham, CL Leung, RS Federman, RJ Green, V Xue, ... US Patent App. 18/151,088, 2023	3	2023
Antigen binding molecules targeting SARS-CoV-2 G Grigoryan, J Ingraham US Patent 11,987,616, 2024	2	2024
Probabilistic Models of Structure in Biological Sequences JB Ingraham PQDT-Global, 2018	2	2018
Antigen binding molecules targeting SARS-CoV-2 G Grigoryan, J Ingraham, CL Leung, RS Federman, RJ Green, AH Ramos, ... US Patent 11,981,725, 2024		2024
Antigen binding molecules targeting sars-cov-2 G GRIGORYAN, J Ingraham, CL Leung, RS FEDERMAN, RJ GREEN, ...		2022

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