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We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures.
An empirical Bayes approach to statistics
Robbins, H. E · 1956
Earlier work this paper cites.
An empirical Bayes estimator of the mean of a normal population
Miyasawa, K · 1961
Earlier work this paper cites.
Uff, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappé, A. K., Casewit, C. J., Colwell, K., Goddard III, W. A., and Skiff, W. M · 1992
Earlier work this paper cites.
Structure-based drug design
Blundell, T. L · 1996
Earlier work this paper cites.
Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94
Halgren, T. A · 1996
Earlier work this paper cites.
The process of structure-based drug design
Anderson, A. C · 2003
Earlier work this paper cites.
Virtual exploration of the small-molecule chemical universe below 160 daltons
Fink, T., Bruggesser, H., and Reymond, J.-L · 2005
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Ertl, P. and Schuffenhauer, A · 2009
Earlier work this paper cites.
Open babel: An open chemical toolbox
O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., and Hutchison, G. R · 2011
Earlier work this paper cites.
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., and Hopkins, A. L · 2012
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
On the modeling of polar component of solvation energy using smooth gaussian-based dielectric function
Li, L., Li, C., and Alexov, E · 2014
Earlier work this paper cites.
Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Bajusz, D., Rácz, A., and Héberger, K · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Alphaspace: Fragment-centric topographical mapping to target protein–protein interaction interfaces
Rooklin, D., Wang, C., Katigbak, J., Arora, P. S., and Zhang, Y · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Rdkit: Open-source cheminformatics software, 2016
Landrum, G · 2016
Earlier work this paper cites.
Langevin dynamics with variable coefficients and nonconservative forces: from stationary states to numerical methods
Sachs, M., Leimkuhler, B., and Danos, V · 2017
Earlier work this paper cites.
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M. and Söding, J · 2017
Earlier work this paper cites.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S., Uchibe, E., and Doya, K · 2018
Earlier work this paper cites.
Generating equilibrium molecules with deep neural networks
Gebauer, N., Gastegger, M., and Schütt, K. T · 2018
Earlier work this paper cites.
3D steerable cnns: Learning rotationally equivariant features in volumetric data
Weiler, M., Geiger, M., Welling, M., Boomsma, W., and Cohen, T. S · 2018
Cited alongside, same era.
Symmetry-adapted generation of 3D point sets for the targeted discovery of molecules
Gebauer, N., Gastegger, M., and Schütt, K · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
Cited alongside, same era.
Ultra-large library docking for discovering new chemotypes
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
Cited alongside, same era.
Neural empirical Bayes
Saremi, S. and Hyvärinen, A · 2019
Generating 3D molecules for target protein binding
Liu, M., Luo, Y., Uchino, K., Maruhashi, K., and Ji, S · 2022
Later among the works it cites.
Zero-shot 3d drug design by sketching and generating
Long, S., Zhou, Y., Dai, X., and Zhou, H · 2022
Later among the works it cites.
Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
Later among the works it cites.
An autoregressive flow model for 3D molecular geometry generation from scratch
Luo, Y. and Ji, S · 2022
Later among the works it cites.
Pyuul provides an interface between biological structures and deep learning algorithms
Orlando, G., Raimondi, D., Duran-Romaña, R., Moreau, Y., Schymkowitz, J., and Rousseau, F · 2022
Later among the works it cites.
Pocket2mol: Efficient molecular sampling based on 3D protein pockets
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Shape-based generative modeling for de novo drug design
Skalic, M., Jiménez, J., Sabbadin, D., and De Fabritiis, G · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
Francoeur, P. G., Masuda, T., Sunseri, J., Jia, A., Iovanisci, R. B., Snyder, I., and Koes, D. R · 2020
Cited alongside, same era.
Equivariant flows: exact likelihood generative learning for symmetric densities
Köhler, J., Klein, L., and Noé, F · 2020
Cited alongside, same era.
Learning a continuous representation of 3D molecular structures with deep generative models
Ragoza, M., Masuda, T., and Koes, D. R · 2020
Cited alongside, same era.
Atom3D: Tasks on molecules in three dimensions
Townshend, R. J., Vögele, M., Suriana, P., Derry, A., Powers, A., Laloudakis, Y., Balachandar, S., Jing, B., Anderson, B., Eismann, S., et al · 2020
Cited alongside, same era.
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J., and Ma, J · 2022
Later among the works it cites.
Incompleteness of graph convolutional neural networks for points clouds in three dimensions
Pozdnyakov, S. N. and Ceriotti, M · 2022
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Generating 3D molecules conditional on receptor binding sites with deep generative models
Ragoza, M., Masuda, T., and Koes, D. R · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Structure-based drug design with equivariant diffusion models
Schneuing, A., Du, Y., Harris, C., Jamasb, A., Igashov, I., Du, W., Blundell, T., Lió, P., Gomes, C., Welling, M., et al · 2022
Later among the works it cites.
Flam-Shepherd, D. and Aspuru-Guzik, A · 2023
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Benchmarking generated poses: How rational is structure-based drug design with generative models?
Harris, C., Didi, K., Jamasb, A. R., Joshi, C. K., Mathis, S. V., Lio, P., and Blundell, T · 2023
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3D molecule generation by denoising voxel grids
Pinheiro, P. O., Rackers, J., Kleinhenz, J., Maser, M., Mahmood, O., Watkins, A. M., Ra, S., Sresht, V., and Saremi, S · 2023
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Geometric deep learning for structure-based ligand design
Powers, A. S., Yu, H. H., Suriana, P., Koodli, R. V., Lu, T., Paggi, J. M., and Dror, R. O · 2023
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Structure-based drug design via semi-equivariant conditional normalizing flows
Rozenberg, E., Rivlin, E., and Freedman, D · 2023
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Universal smoothed score functions for generative modeling
Saremi, S., Srivastava, R. K., and Bach, F · 2023
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The surprising effectiveness of diffusion models for optical flow and monocular depth estimation
Saxena, S., Herrmann, C., Hur, J., Kar, A., Norouzi, M., Sun, D., and Fleet, D. J · 2023
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Integrating structure-based approaches in generative molecular design
Thomas, M., Bender, A., and de Graaf, C · 2023
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Midi: Mixed graph and 3D denoising diffusion for molecule generation
Vignac, C., Osman, N., Toni, L., and Frossard, P · 2023
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Generating molecular conformer fields
Wang, Y., Elhag, A. A., Jaitly, N., Susskind, J. M., and Bautista, M. A · 2023
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Geometric latent diffusion models for 3D molecule generation
Xu, M., Powers, A., Dror, R., Ermon, S., and Leskovec, J · 2023
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Molecule generation for target protein binding with structural motifs
Zhang, Z., Min, Y., Zheng, S., and Liu, Q · 2023
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Protein discovery with discrete walk-jump sampling
Frey, N. C., Berenberg, D., Kleinhenz, J., Hotzel, I., Lafrance-Vanasse, J., Kelly, R. L., Wu, Y., Rajpal, A., Ra, S., Bonneau, R., Cho, K., Loukas, A., Gligorijevic, V., and Saremi, S · 2024
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