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Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials.
Natural selection and the concept of a protein space
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Reverse-time diffusion equation models
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Time reversal of diffusions
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The art and practice of structure-based drug design: a molecular modeling perspective
Bohacek, R. S., McMartin, C., and Guida, W. C · 1996
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Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups
Ertl, P · 2003
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P · 2005
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Erg: 2d pharmacophore descriptions for scaffold hopping
Stiefl, N., Watson, I. A., Baumann, K., and Zaliani, A · 2006
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Matplotlib: A 2d graphics environment
Hunter, J. D · 2007
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Biopython: freely available python tools for computational molecular biology and bioinformatics
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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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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M · 2010
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Machine learning: an algorithmic perspective
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 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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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Landrum, G. et al · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Sabdab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., and Deane, C. M · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Jimenez Rezende, D., and Welling, M · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Fast, accurate, and reliable molecular docking with QuickVina 2
Alhossary, A., Handoko, S. D., Mu, Y., and Kwoh, C.-K · 2015
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Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Bajusz, D., Rácz, A., and Héberger, K · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2015
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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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Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., and Saenko, K · 2015
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Anarci: antigen receptor numbering and receptor classification
Dunbar, J. and Deane, C. M · 2016
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., and Lempitsky, V · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
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The chembl database in 2017
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., et al · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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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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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2018
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Protgpt2 is a deep unsupervised language model for protein design
Ferruz, N., Schmidt, S., and Höcker, B · 2022
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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Riemannian diffusion models
Huang, C.-W., Aghajohari, M., Bose, J., Panangaden, P., and Courville, A. C · 2022
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Qmugs, quantum mechanical properties of drug-like molecules
Isert, C., Atz, K., Jiménez-Luna, J., and Schneider, G · 2022
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Torsional diffusion for molecular conformer generation
Jing, B., Corso, G., Chang, J., Barzilay, R., and Jaakkola, T. S · 2022
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UMAP: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., and Melville, J · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Sanchez-Lengeling, B. and Aspuru-Guzik, A · 2018
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C. A · 2019
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Contrastive adaptation network for unsupervised domain adaptation
Kang, G., Jiang, L., Yang, Y., and Hauptmann, A. G · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 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
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jo, J., Lee, S., and Hwang, S. J · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Observed antibody space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences
Olsen, T. H., Boyles, F., and Deane, C. M · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 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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Tractable function-space variational inference in Bayesian neural networks
Rudner, T. G., Chen, Z., Teh, Y. W., and Gal, Y · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
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Plex: Towards reliability using pretrained large model extensions
Tran, D., Liu, J., Dusenberry, M. W., Phan, D., Collier, M., Ren, J., Han, K., Wang, Z., Mariet, Z., Hu, H., Band, N., Rudner, T. G. J., Singhal, K., Nado, Z., van Amersfoort, J., Kirsch, A., Jenatton, R., Thain, N., Yuan, H., Buchanan, K., Murphy, K., Sculley, D., Gal, Y., Ghahramani, Z., Snoek, J., and Lakshminarayanan, B · 2022
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Dual use of artificial-intelligence-powered drug discovery
Urbina, F., Lentzos, F., Invernizzi, C., and Ekins, S · 2022
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The compas project: A computational database of polycyclic aromatic systems. phase 1: cata-condensed polybenzenoid hydrocarbons
Wahab, A., Pfuderer, L., Paenurk, E., and Gershoni-Poranne, R · 2022
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Corso, G., Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T. S · 2023
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Protein design with guided discrete diffusion
Gruver, N., Stanton, S., Frey, N. C., Rudner, T. G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A. G · 2023
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DecompDiff: Diffusion models with decomposed priors for structure-based drug design
Guan, J., Zhou, X., Yang, Y., Bao, Y., Peng, J., Ma, J., Liu, Q., Wang, L., and Gu, Q · 2023
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Illuminating protein space with a programmable generative model
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al · 2023
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Drugood: Out-of-distribution dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations
Ji, Y., Zhang, L., Wu, J., Wu, B., Li, L., Huang, L.-K., Xu, T., Rong, Y., Ren, J., Xue, D., et al · 2023
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Drug discovery under covariate shift with domain-informed prior distributions over functions
Klarner, L., Rudner, T. G., Reutlinger, M., Schindler, T., Morris, G. M., Deane, C., and Teh, Y. W · 2023
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Exploring chemical space with score-based out-of-distribution generation
Lee, S., Jo, J., and Hwang, S. J · 2023
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Reflected diffusion models
Lou, A. and Ermon, S · 2023
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MolDiff: Addressing the atom-bond inconsistency problem in 3D molecule diffusion generation
Peng, X., Guan, J., Liu, Q., and Ma, J · 2023
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Function-space regularization in neural networks: A probabilistic perspective
Rudner, T. G., Kapoor, S., Qiu, S., and Wilson, A. G · 2023
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ZINC-22 - a free multi-billion-scale database of tangible compounds for ligand discovery
Tingle, B. I., Tang, K. G., Castanon, M., Gutierrez, J. J., Khurelbaatar, M., Dandarchuluun, C., Moroz, Y. S., and Irwin, J. J · 2023
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Scientific discovery in the age of artificial intelligence
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2023
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Guided diffusion for inverse molecular design
Weiss, T., Mayo Yanes, E., Chakraborty, S., Cosmo, L., Bronstein, A. M., and Gershoni-Poranne, R · 2023
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Geometric latent diffusion models for 3d molecule generation
Xu, M., Powers, A. S., Dror, R. O., Ermon, S., and Leskovec, J · 2023
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Pre-training via denoising for molecular property prediction
Zaidi, S., Schaarschmidt, M., Martens, J., Kim, H., Teh, Y. W., Sanchez-Gonzalez, A., Battaglia, P., Pascanu, R., and Godwin, J · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L., Rao, A., and Agrawala, M · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al · 2024
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Generalization in diffusion models arises from geometry-adaptive harmonic representations
Kadkhodaie, Z., Guth, F., Simoncelli, E. P., and Mallat, S · 2024
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