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Drug discovery is a complex process that involves multiple stages and tasks.
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
Geman, S. and Geman, D · 1984
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
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On the art of compiling and using ’drug-like’ chemical fragment spaces
Degen, J., Wegscheid-Gerlach, C., Zaliani, A., and Rarey, M · 2008
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Ertl, P. and Schuffenhauer, A · 2009
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The rise of fragment-based drug discovery
Murray, C. W. and Rees, D. C · 2009
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Principles of early drug discovery
Hughes, J. P., Rees, S., Kalindjian, S. B., and Philpott, K. L · 2011
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Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., and Hopkins, A. L · 2012
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Zinc: a free tool to discover chemistry for biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., and Coleman, R. G · 2012
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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
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Unichem: a unified chemical structure cross-referencing and identifier tracking system
Chambers, J., Davies, M., Gaulton, A., Hersey, A., Velankar, S., Petryszak, R., Hastings, J., Bellis, L., McGlinchey, S., and Overington, J. P · 2013
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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RDKit: Open-source cheminformatics software, 2016
Landrum, G. et al · 2016
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Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., and Chen, H · 2017
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jensen, J. H · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Huggingface’s transformers: State-of-the-art natural language processing. arxiv
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2019
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Multi-objective molecule generation using interpretable substructures
Jin, W., Barzilay, R., and Jaakkola, T · 2020
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Application of fragment-based drug discovery to versatile targets
Li, Q · 2020
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Mars: Markov molecular sampling for multi-objective drug discovery
Xie, Y., Shi, C., Zhou, H., Yang, Y., Zhang, W., Yu, Y., and Li, L · 2020
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Syntalinker: automatic fragment linking with deep conditional transformer neural networks
Yang, Y., Zheng, S., Su, S., Zhao, C., Xu, J., and Chen, H · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and Van Den Berg, R · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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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Genetic algorithms are strong baselines for molecule generation
Tripp, A. and Hernández-Lobato, J. M · 2023
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Digress: Discrete denoising diffusion for graph generation
Vignac, C., Krawczuk, I., Siraudin, A., Wang, B., Cevher, V., and Frossard, P · 2023
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Retrieval-based controllable molecule generation
Wang, Z., Nie, W., Qiao, Z., Xiao, C., Baraniuk, R., and Anandkumar, A · 2023
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Diffmol: 3d structured molecule generation with discrete denoising diffusion probabilistic models
Zhang, W., Wang, X., Smith, J., Eaton, J., Rees, B., and Gu, Q · 2023
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Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
Cited alongside, same era.
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y. H., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., and Zitnik, M · 2021
Cited alongside, same era.
Learning to extend molecular scaffolds with structural motifs
Maziarz, K., Jackson-Flux, H. R., Cameron, P., Sirockin, F., Schneider, N., Stiefl, N., Segler, M., and Brockschmidt, M · 2021
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Hit and lead discovery with explorative rl and fragment-based molecule generation
Yang, S., Hwang, D., Lee, S., Ryu, S., and Hwang, S. J · 2021
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A continuous time framework for discrete denoising models
Campbell, A., Benton, J., De Bortoli, V., Rainforth, T., Deligiannidis, G., and Doucet, A · 2022
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Maskgit: Masked generative image transformer
Chang, H., Zhang, H., Jiang, L., Liu, C., and Freeman, W. T · 2022
Cited alongside, same era.
Gruver, N., Stanton, S., Frey, N., Rudner, T. G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A. G · 2024
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Simulating 500 million years of evolution with a language model
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al · 2024
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Mudiff: Unified diffusion for complete molecule generation
Hua, C., Luan, S., Xu, M., Ying, Z., Fu, J., Ermon, S., and Precup, D · 2024
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Guiding a diffusion model with a bad version of itself
Karras, T., Aittala, M., Kynkäänniemi, T., Lehtinen, J., Aila, T., and Laine, S · 2024
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Genetic-guided gflownets: Advancing in practical molecular optimization benchmark
Kim, H., Kim, M., Choi, S., and Park, J · 2024
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Diffbp: Generative diffusion of 3d molecules for target protein binding
Lin, H., Huang, Y., Zhang, O., Ma, S., Liu, M., Li, X., Wu, L., Ji, S., Hou, T., and Li, S. Z · 2024
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Discrete diffusion language modeling by estimating the ratios of the data distribution
Lou, A., Meng, C., and Ermon, S · 2024
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Unlocking guidance for discrete state-space diffusion and flow models
Nisonoff, H., Xiong, J., Allenspach, S., and Listgarten, J · 2024
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Gotta be safe: a new framework for molecular design
Noutahi, E., Gabellini, C., Craig, M., Lim, J. S., and Tossou, P · 2024
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Simple and effective masked diffusion language models
Sahoo, S. S., Arriola, M., Schiff, Y., Gokaslan, A., Marroquin, E., Chiu, J. T., Rush, A., and Kuleshov, V · 2024
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Simplified and generalized masked diffusion for discrete data
Shi, J., Han, K., Wang, Z., Doucet, A., and Titsias, M. K · 2024
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The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods
Zdrazil, B., Felix, E., Hunter, F., Manners, E. J., Blackshaw, J., Corbett, S., de Veij, M., Ioannidis, H., Lopez, D. M., Mosquera, J. F., et al · 2024
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A reparameterized discrete diffusion model for text generation
Zheng, L., Yuan, J., Yu, L., and Kong, L · 2024
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