| Progen2: exploring the boundaries of protein language models E Nijkamp, JA Ruffolo, EN Weinstein, N Naik, A Madani Cell systems 14 (11), 968-978. e3, 2023 | 145* | 2023 |
| Antibody structure prediction using interpretable deep learning JA Ruffolo, J Sulam, JJ Gray Patterns 3 (2), 2022 | 141 | 2022 |
| Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies JA Ruffolo, LS Chu, SP Mahajan, JJ Gray Nature communications 14 (1), 2389, 2023 | 136 | 2023 |
| Deciphering antibody affinity maturation with language models and weakly supervised learning JA Ruffolo, JJ Gray, J Sulam arXiv preprint arXiv:2112.07782, 2021 | 72 | 2021 |
| IgLM: Infilling language modeling for antibody sequence design RW Shuai, JA Ruffolo, JJ Gray Cell Systems 14 (11), 979-989. e4, 2023 | 70* | 2023 |
| Geometric Potentials from Deep Learning Improve Prediction of CDR H3 Loop Structures JA Ruffolo, C Guerra, SP Mahajan, J Sulam, JJ Gray Bioinformatics 36 (Supplement_1), i268–i275, 2020 | 60 | 2020 |
| Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE (3)-Discrete Diffusion A Morehead, J Ruffolo, A Bhatnagar, A Madani arXiv preprint arXiv:2401.06151, 2023 | 28 | 2023 |
| Hallucinating structure-conditioned antibody libraries for target-specific binders SP Mahajan, JA Ruffolo, R Frick, JJ Gray bioRxiv, 2022.06. 06.494991, 2022 | 12 | 2022 |
| Simultaneous prediction of antibody backbone and side-chain conformations with deep learning D Akpinaroglu, JA Ruffolo, SP Mahajan, JJ Gray PloS one 17 (6), e0258173, 2022 | 11* | 2022 |
| Designing proteins with language models JA Ruffolo, A Madani Nature Biotechnology 42 (2), 200-202, 2024 | 6 | 2024 |
| Flexible protein–protein docking with a multitrack iterative transformer LS Chu, JA Ruffolo, A Harmalkar, JJ Gray Protein Science 33 (2), e4862, 2024 | 6 | 2024 |
| Enhancement of antibody thermostability and affinity by computational design in the absence of antigen M Hutchinson, JA Ruffolo, N Haskins, M Iannotti, G Vozza, T Pham, ... bioRxiv, 2023.12. 19.572421, 2023 | 1 | 2023 |
| A versatile design platform for glycoengineering therapeutic antibodies SD Ludwig, ZJ Bernstein, C Agatemor, K Dammen-Brower, J Ruffolo, ... Mabs 14 (1), 2095704, 2022 | 1 | 2022 |
| Towards deep learning models for target-specific antibody design SP Mahajan, J Ruffolo, R Frick, JJ Gray Biophysical Journal 121 (3), 528a, 2022 | 1 | 2022 |
| Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences JA Ruffolo, S Nayfach, J Gallagher, A Bhatnagar, J Beazer, R Hussain, ... bioRxiv, 2024.04. 22.590591, 2024 | | 2024 |
| FLAb: Benchmarking deep learning methods for antibody fitness prediction M Chungyoun, JA Ruffolo, JJ Gray bioRxiv, 2024.01. 13.575504, 2024 | | 2024 |
| Contextual protein and antibody encodings from equivariant graph transformers SP Mahajan, JA Ruffolo, JJ Gray bioRxiv, 2023 | | 2023 |
| DEEP LEARNING METHODS FOR ANTIBODY STRUCTURE PREDICTION AND DESIGN JA Ruffolo Johns Hopkins University, 2023 | | 2023 |
| Track: Machine Learning in Protein Science Hallucinating native-like antibodies with deep learning SP Mahajan, JA Ruffolo, R Frick, JJ Gray PROTEIN SCIENCE 32, 2023 | | 2023 |
| Hallucinating Inexpensive, Diverse and Native-like Antibody Binders with Deep Learning SP Mahajan, J Ruffolo, R Frick, JJ Gray 2022 AIChE Annual Meeting, 2022 | | 2022 |
Jeffrey J. GrayJohns Hopkins University, Chemical and Biomolecular Engineering / Program in Molecular BiophysicsVerified email at jhu.edu
