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In recent years, there has been a surge in the development of 3D structure-based pre-trained protein models, representing a significant advancement over pre-trained protein language models in various downstream tasks.
Evolutionary motif and its biological and structural significance
Tateno, Y., Ikeo, K., Imanishi, T., Watanabe, H., Endo, T., Yamaguchi, Y., Suzuki, Y., Takahashi, K., Tsunoyama, K., Kawai, M., et al · 1997
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Sitehound-web: a server for ligand binding site identification in protein structures
Hernandez, M., Ghersi, D., and Sanchez, R · 2009
Earlier work this paper cites.
Fpocket: An open source platform for ligand pocket detection
Le Guilloux, V., Schmidtke, P., and Tuffery, P · 2009
Earlier work this paper cites.
Dnabind: A hybrid algorithm for structure-based prediction of dna-binding residues by combining machine learning-and template-based approaches
Liu, R. and Hu, J · 2013
Earlier work this paper cites.
Rnabindrplus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted rna-binding residues in proteins
Walia, R. R., Xue, L. C., Wilkins, K., El-Manzalawy, Y., Dobbs, D., and Honavar, V · 2014
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A large-scale assessment of nucleic acids binding site prediction programs
Miao, Z. and Westhof, E · 2015
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Predicting protein-dna binding residues by weightedly combining sequence-based features and boosting multiple svms
Hu, J., Li, Y., Zhang, M., Yang, X., Shen, H.-B., and Yu, D.-J · 2016
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FreeSASA: An open source c library for solvent accessible surface area calculations
Mitternacht, S · 2016
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The rcsb protein data bank: integrative view of protein, gene and 3d structural information
Rose, P. W., Prlić, A., Altunkaya, A., Bi, C., Bradley, A. R., Christie, C. H., Costanzo, L. D., Duarte, J. M., Dutta, S., Feng, Z., et al · 2016
Earlier work this paper cites.
Deepsite: protein-binding site predictor using 3d-convolutional neural networks
Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., and De Fabritiis, G · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks, 2018
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Krivák, R. and Hoksza, D · 2018
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Coach-d: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking
Wu, Q., Peng, Z., Zhang, Y., and Yang, J · 2018
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A deep learning framework to predict binding preference of rna constituents on protein surface
Lam, J. H., Li, Y., Zhu, L., Umarov, R., Jiang, H., Héliou, A., Sheong, F. K., Liu, T., Long, Y., Li, Y., et al · 2019
Earlier work this paper cites.
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies
Macari, G., Toti, D., and Polticelli, F · 2019
Earlier work this paper cites.
Improving the prediction of protein–nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods
Su, H., Liu, M., Sun, S., Peng, Z., and Yang, J · 2019
Cited alongside, same era.
Dnapred: accurate identification of dna-binding sites from protein sequence by ensembled hyperplane-distance-based support vector machines
Zhu, Y.-H., Hu, J., Song, X.-N., and Yu, D.-J · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., Bhowmik, D., and Rost, B · 2020
Cited alongside, same era.
Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures, 2020
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction, June 2022
Stärk, H., Ganea, O.-E., Pattanaik, L., Barzilay, R., and Jaakkola, T · 2022
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Input-level inductive biases for 3d reconstruction, 2022
Yifan, W., Doersch, C., Arandjelović, R., Carreira, J., and Zisserman, A · 2022
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Cross-modality and self-supervised protein embedding for compound–protein affinity and contact prediction
You, Y. and Shen, Y · 2022
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xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein, July 2023a
Chen, B., Cheng, X., Geng, Y.-a., Li, S., Zeng, X., Wang, B., Gong, J., Liu, C., Zeng, A., Dong, Y., Tang, J., and Song, L · 2023
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DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking, February 2023
Corso, G., Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T · 2023
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Hermosilla, P., Schäfer, M., Lang, M., Fackelmann, G., Vázquez, P.-P., Kozlikova, B., Krone, M., Ritschel, T., and Ropinski, T · 2020
Cited alongside, same era.
Equivariant Graph Neural Networks for 3D Macromolecular Structure, July 2021
Jing, B., Eismann, S., Soni, P. N., and Dror, R. O · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D · 2021
Cited alongside, same era.
Fast and sensitive taxonomic assignment to metagenomic contigs
Mirdita, M., Steinegger, M., Breitwieser, F., Söding, J., and Levy Karin, E · 2021
Cited alongside, same era.
Msa transformer, 2021
Rao, R. M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., and Rives, A · 2021
Cited alongside, same era.
Graphbind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues
Xia, Y., Xia, C.-Q., Pan, X., and Shen, H.-B · 2021
Cited alongside, same era.
Hotprotein: A novel framework for protein thermostability prediction and editing
Chen, T., Gong, C., Diaz, D. J., Chen, X., Wells, J. T., Wang, Z., Ellington, A., Dimakis, A., Klivans, A., et al · 2022
Cited alongside, same era.
Continuous-discrete convolution for geometry-sequence modeling in proteins
Fan, H., Wang, Z., Yang, Y., and Kankanhalli, M · 2022
Cited alongside, same era.
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Huang, Y., Wu, L., Lin, H., Zheng, J., Wang, G., and Li, S. Z · 2023
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Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom, October 2023
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., McHugh, R., Vafeados, D., Li, X., Sutherland, G. A., Hitchcock, A., Hunter, C. N., Baek, M., DiMaio, F., and Baker, D · 2023
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Pre-training sequence, structure, and surface features for comprehensive protein representation learning
Lee, Y., Yu, H., Lee, J., Kim, J., and Brain, K · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al · 2023
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Highly accurate quantum chemical property prediction with uni-mol+
Lu, S., Gao, Z., He, D., Zhang, L., and Ke, G · 2023
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Fabind: Fast and accurate protein-ligand binding
Pei, Q., Gao, K., Wu, L., Zhu, J., Xia, Y., Xie, S., Qin, T., He, K., Liu, T.-Y., and Yan, R · 2023
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Learning hierarchical protein representations via complete 3d graph networks
Wang, L., Liu, H., Liu, Y., Kurtin, J., and Ji, S · 2023
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Full-atom protein pocket design via iterative refinement
ZHANG, Z., Lu, Z., Hao, Z., Zitnik, M., and Liu, Q · 2023
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Pre-training protein encoder via siamese sequence-structure diffusion trajectory prediction
Zhang, Z., Xu, M., Lozano, A., Chenthamarakshan, V., Das, P., and Tang, J · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2023
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