Fetching the paper…
Reading the bibliography…
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging.
Prevention of venous thromboembolism
Gallus, A. S. and Hirsh, J · 1976
Earlier work this paper cites.
Pymol: An open-source molecular graphics tool
DeLano, W. L. et al · 2002
Earlier work this paper cites.
Zdock: an initial-stage protein-docking algorithm
Chen, R., Li, L., and Weng, Z · 2003
Earlier work this paper cites.
Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine
Jain, A. N · 2003
Earlier work this paper cites.
Fds: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function
Taylor, R. D., Jewsbury, P. J., and Essex, J. W · 2003
Earlier work this paper cites.
Improved protein–ligand docking using gold
Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W., and Taylor, R. D · 2003
Earlier work this paper cites.
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
Earlier work this paper cites.
Novel procedure for modeling ligand/receptor induced fit effects
Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A., and Farid, R · 2006
Earlier work this paper cites.
Blast: improvements for better sequence analysis
Ye, J., McGinnis, S., and Madden, T. L · 2006
Earlier work this paper cites.
Docking and scoring with alternative side-chain conformations
Hartmann, C., Antes, I., and Lengauer, T · 2009
Earlier work this paper cites.
The haddock web server for data-driven biomolecular docking
De Vries, S. J., Van Dijk, M., and Bonvin, A. M · 2010
Earlier work this paper cites.
Directory of useful decoys, enhanced (dud-e): better ligands and decoys for better benchmarking
Mysinger, M. M., Carchia, M., Irwin, J. J., and Shoichet, B. K · 2012
Earlier work this paper cites.
Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
Koes, D. R., Baumgartner, M. P., and Camacho, C. J · 2013
Earlier work this paper cites.
Discovery of zap70 inhibitors by high-throughput docking into a conformation of its kinase domain generated by molecular dynamics
Zhao, H. and Caflisch, A · 2013
Earlier work this paper cites.
Protein-protein docking: From interaction to interactome
Vakser, I. A · 2014
Earlier work this paper cites.
Fast, accurate, and reliable molecular docking with quickvina 2
Alhossary, A., Handoko, S. D., Mu, Y., and Kwoh, C.-K · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Hdock: a web server for protein–protein and protein–dna/rna docking based on a hybrid strategy
Yan, Y., Zhang, D., Zhou, P., Li, B., and Huang, S.-Y · 2017
Earlier work this paper cites.
P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Krivák, R. and Hoksza, D · 2018
Earlier work this paper cites.
Inherent versus induced protein flexibility: comparisons within and between apo and holo structures
Clark, J. J., Benson, M. L., Smith, R. D., and Carlson, H. A · 2019
Earlier work this paper cites.
Deepdock: enhancing ligand-protein interaction prediction by a combination of ligand and structure information
Liao, Z., You, R., Huang, X., Yao, X., Huang, T., and Zhu, S · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Cited alongside, same era.
Aggarwal, R., Gupta, A., and Priyakumar, U · 2021
Cited alongside, same era.
Rcsb protein data bank: powerful new tools for exploring 3d structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al · 2021
Cited alongside, same era.
Amber 2021
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
Later among the works it cites.
Equibind: Geometric deep learning for drug binding structure prediction, 2022
Stärk, H., Ganea, O.-E., Pattanaik, L., Barzilay, R., and Jaakkola, T · 2022
Later among the works it cites.
Physics-informed deep neural network for rigid-body protein docking
Sverrisson, F., Feydy, J., Southern, J., Bronstein, M. M., and Correia, B. E · 2022
Later among the works it cites.
Plantain: Diffusion-inspired pose score minimization for fast and accurate molecular docking
Brocidiacono, M., Popov, K. I., Koes, D. R., and Tropsha, A · 2023
Later among the works it cites.
Diffdock: Diffusion steps, twists, and turns for molecular docking
Corso, G., Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Case, D. A., Aktulga, H. M., Belfon, K., Ben-Shalom, I., Brozell, S. R., Cerutti, D. S., Cheatham III, T. E., Cruzeiro, V. W. D., Darden, T. A., Duke, R. E., et al · 2021
Cited alongside, same era.
Diffusion schrödinger bridge with applications to score-based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A · 2021
Cited alongside, same era.
Sunflower trypsin inhibitor-1 (sfti-1): sowing seeds in the fields of chemistry and biology
de Veer, S. J., White, A. M., and Craik, D. J · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Autodock vina 1.2.0: New docking methods, expanded force field, and python bindings
Eberhardt, J., Santos-Martins, D., Tillack, A. F., and Forli, S · 2021
Cited alongside, same era.
Protein complex prediction with alphafold-multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al · 2021
Cited alongside, same era.
Independent se (3)-equivariant models for end-to-end rigid protein docking
Ganea, O.-E., Huang, X., Bunne, C., Bian, Y., Barzilay, R., Jaakkola, T., and Krause, A · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Equivariant flexible modeling of the protein–ligand binding pose with geometric deep learning
Dong, T., Yang, Z., Zhou, J., and Chen, C. Y.-C · 2023
Later among the works it cites.
Linkernet: Fragment poses and linker co-design with 3d equivariant diffusion
Guan, J., Peng, X., Jiang, P., Luo, Y., Peng, J., and Ma, J · 2023
Later among the works it cites.
Learning complete protein representation by deep coupling of sequence and structure
Hu, B., Tan, C., Xia, J., Zheng, J., Huang, Y., Wu, L., Liu, Y., Xu, Y., and Li, S. Z · 2023
Later among the works it cites.
Unsupervised protein-ligand binding energy prediction via neural euler’s rotation equation
Jin, W., Sarkizova, S., Chen, X., Hacohen, N., and Uhler, C · 2023
Later among the works it cites.
Conditional antibody design as 3d equivariant graph translation
Kong, X., Huang, W., and Liu, Y · 2023
Later among the works it cites.
Generalized biomolecular modeling and design with rosettafold all-atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al · 2023
Later among the works it cites.
Deep learning for flexible and site-specific protein docking and design
McPartlon, M. and Xu, J · 2023
Later among the works it cites.
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
Later among the works it cites.
Diffdock-pocket: Diffusion for pocket-level docking with sidechain flexibility
Plainer, M., Toth, M., Dobers, S., Stärk, H., Corso, G., Marquet, C., and Barzilay, R · 2023
Later among the works it cites.
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
Qiao, Z., Nie, W., Vahdat, A., Miller III, T. F., and Anandkumar, A · 2023
Later among the works it cites.
Diffusion schrödinger bridge matching, 2023
Shi, Y., Bortoli, V. D., Campbell, A., and Doucet, A · 2023
Later among the works it cites.
Homophily-enhanced self-supervision for graph structure learning: Insights and directions
Wu, L., Lin, H., Liu, Z., Liu, Z., Huang, Y., and Li, S. Z · 2023
Later among the works it cites.
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
Later among the works it cites.
Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences
Buttenschoen, M., Morris, G. M., and Deane, C. M · 2024
Closest in time.
A review on molecular docking as an interpretative tool for molecular targets in disease management
Sahu, D., Rathor, L. S., Dwivedi, S. D., Shah, K., Chauhan, N. S., Singh, M. R., and Singh, D · 2024
Closest in time.