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A significant amount of protein function requires binding small molecules, including enzymatic catalysis.
Announcing the worldwide Protein Data Bank
Berman, H., Henrick, K., and Nakamura, H · 2003
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Glide: a new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening
Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., and Banks, J. L · 2004
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Lehninger Principles of Biochemistry, Fourth Edition
Nelson, D. L. and Cox, M. M · 2004
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Moldock: a new technique for high-accuracy molecular docking
Thomsen, R. and Christensen, M. H · 2006
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, O. and Olson, A. J · 2010
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Binding pocket optimization by computational protein design
Malisi, C., Schumann, M., Toussaint, N. C., Kageyama, J., Kohlbacher, O., and Höcker, B · 2012
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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PocketOptimizer and the design of ligand binding sites
Stiel, A. C., Nellen, M., and Höcker, B · 2016
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Sampling and energy evaluation challenges in ligand binding protein design
Dou, J., Doyle, L., Greisen, Jr, P., Schena, A., Park, H., Johnsson, K., Stoddard, B. L., and Baker, D · 2017
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Forging the basis for developing protein–ligand interaction scoring functions
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., and Wang, R · 2017
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
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Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
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Euclidean neural networks: e3nn, 2020
Geiger, M., Smidt, T., M., A., Miller, B. K., Boomsma, W., Dice, B., Lapchevskyi, K., Weiler, M., Tyszkiewicz, M., Batzner, S., Uhrin, M., Frellsen, J., Jung, N., Sanborn, S., Rackers, J., and Bailey, M · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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A defined structural unit enables de novo design of small-molecule–binding proteins
Polizzi, N. F. and DeGrado, W. F · 2020
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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., et al · 2021
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Gnina 1.0: molecular docking with deep learning
McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., and Koes, D. R · 2021
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A geometric deep learning approach to predict binding conformations of bioactive molecules
Méndez-Lucio, O., Ahmad, M., del Rio-Chanona, E. A., and Wegner, J. K · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A. K., King, N. P., and Baker, D · 2022
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Equivariant diffusion for molecule generation in 3d, 2022
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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Learning inverse folding from millions of predicted structures
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., and Rives, A · 2022
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Iterative refinement graph neural network for antibody sequence-structure co-design, 2022
Jin, W., Wohlwend, J., Barzilay, R., and Jaakkola, T · 2022
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Atomic context-conditioned protein sequence design using ligandmpnn
Dauparas, J., Lee, G. R., Pecoraro, R., An, L., Anishchenko, I., Glasscock, C., and Baker, D · 2023
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Equivariant flow matching, 2023
Klein, L., Krämer, A., and Noé, F · 2023
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Context-aware geometric deep learning for protein sequence design
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Jing, B., Corso, G., Chang, J., Barzilay, R., and Jaakkola, T · 2022
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Flow matching for generative modeling
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Liu, X., Gong, C., and Liu, Q · 2022
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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Lu, W., Wu, Q., Zhang, J., Rao, J., Li, C., and Zheng, S · 2022
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Stärk, H., Ganea, O.-E., Pattanaik, L., Barzilay, R., and Jaakkola, T · 2022
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Neklyudov, K., Brekelmans, R., Severo, D., and Makhzani, A · 2023
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Plainer, M., Toth, M., Dobers, S., Stark, H., Corso, G., Marquet, C., and Barzilay, R · 2023
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Pooladian, A.-A., Ben-Hamu, H., Domingo-Enrich, C., Amos, B., Lipman, Y., and Chen, R. T. Q · 2023
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Verma, Y., Heinonen, M., and Garg, V · 2023
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Yeh, A. H.-W., Norn, C., Kipnis, Y., Tischer, D., Pellock, S. J., Evans, D., Ma, P., Lee, G. R., Zhang, J. Z., Anishchenko, I., Coventry, B., Cao, L., Dauparas, J., Halabiya, S., DeWitt, M., Carter, L., Houk, K. N., and Baker, D · 2023
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