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Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.
Environment and exposure to solvent of protein atoms. lysozyme and insulin
Shrake, A. and Rupley, J. A · 1973
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Geostatistics for natural resources evaluation
Goovaerts, P. et al · 1997
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J · 1997
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Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M., and Welch, W. J · 1998
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A fast and elitist multiobjective genetic algorithm: Nsga-ii
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T · 2002
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Text classification using string kernels
Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., and Watkins, C · 2002
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Single-and multi-objective evolutionary design optimization assisted by gaussian random field metamodels
Emmerich, M · 2005
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The foldx web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., and Serrano, L · 2005
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Gaussian processes for machine learning , volume 2
Rasmussen, C. E. and Williams, C. K · 2006
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Multi-task gaussian process prediction
Bonilla, E. V., Chai, K., and Williams, C · 2007
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Fluorescent proteins and their applications in imaging living cells and tissues
Chudakov, D. M., Matz, M. V., Lukyanov, S., and Lukyanov, K. A · 2009
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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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Brochu, E., Cora, V. M., and De Freitas, N · 2010
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Sparse spectrum gaussian process regression
Lázaro-Gredilla, M., Quinonero-Candela, J., Rasmussen, C. E., and Figueiras-Vidal, A. R · 2010
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Gaussian process optimization in the bandit setting: no regret and experimental design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M · 2010
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Kernels for vector-valued functions: A review
Alvarez, M. A., Rosasco, L., and Lawrence, N. D · 2011
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Hypervolume-based expected improvement: Monotonicity properties and exact computation
Emmerich, M. T., Deutz, A. H., and Klinkenberg, J. W · 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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Gaussian processes for big data
Hensman, J., Fusi, N., and Lawrence, N. D · 2013
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Pareto frontier learning with expensive correlated objectives
Shah, A. and Ghahramani, Z · 2016
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Unbounded bayesian optimization via regularization
Shahriari, B., Bouchard-Côté, A., and Freitas, N · 2016
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Bayesian optimization in a billion dimensions via random embeddings
Wang, Z., Hutter, F., Zoghi, M., Matheson, D., and de Feitas, N · 2016
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Learning kernels over strings using gaussian processes
Beck, D. and Cohn, T · 2017
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Multi-fidelity bayesian optimisation with continuous approximations
Kandasamy, K., Dasarathy, G., Schneider, J., and Póczos, B · 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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A tutorial on bayesian optimization
Frazier, P. I · 2018
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G · 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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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Krenn, M., Häse, F., Nigam, A., Friederich, P., and Aspuru-Guzik, A · 2020
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Boss: Bayesian optimization over string spaces
Moss, H. B., Beck, D., Gonzalez, J., Leslie, D. S., and Rayson, P · 2020
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Latent-variable non-autoregressive neural machine translation with deterministic inference using a delta posterior
Shu, R., Lee, J., Nakayama, H., and Cho, K · 2020
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Sample-efficient optimization in the latent space of deep generative models via weighted retraining
Tripp, A., Daxberger, E., and Hernández-Lobato, J. M · 2020
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Estimated research and development investment needed to bring a new medicine to market, 2009-2018
Wouters, O. J., McKee, M., and Luyten, J · 2020
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Deterministic non-autoregressive neural sequence modeling by iterative refinement
Lee, J., Mansimov, E., and Cho, K · 2018
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Role of solvent accessibility for aggregation-prone patches in protein folding
Mishra, A., Ranganathan, S., Jayaram, B., and Sattar, A · 2018
Cited alongside, same era.
The uncertainty bellman equation and exploration
O’Donoghue, B., Osband, I., Munos, R., and Mnih, V · 2018
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Scalable global optimization via local bayesian optimization
Eriksson, D., Pearce, M., Gardner, J., Turner, R. D., and Poloczek, M · 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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Fpbase: a community-editable fluorescent protein database
Lambert, T. J · 2019
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Constrained bayesian optimization with noisy experiments
Letham, B., Karrer, B., Ottoni, G., and Bakshy, E · 2019
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Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al · 2021
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Low-n protein engineering with data-efficient deep learning
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., and Church, G. M · 2021
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The hunt for red fluorescent proteins
Dance, A · 2021
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Combining latent space and structured kernels for bayesian optimization over combinatorial spaces
Deshwal, A. and Doppa, J · 2021
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Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design
Gao, W., Mercado, R., and Coley, C. W · 2021
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Function-guided protein design by deep manifold sampling
Gligorijevic, V., Berenberg, D., Ra, S., Watkins, A., Kelow, S., Cho, K., and Bonneau, R · 2021
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High-dimensional bayesian optimisation with variational autoencoders and deep metric learning
Grosnit, A., Tutunov, R., Maraval, A. M., Griffiths, R.-R., Cowen-Rivers, A. I., Yang, L., Zhu, L., Lyu, W., Chen, Z., Wang, J., et al · 2021
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Predicting and interpreting large scale mutagenesis data using analyses of protein stability and conservation
Høie, M. H., Cagiada, M., Frederiksen, A. H. B., Stein, A., and Lindorff-Larsen, K · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., and Zitnik, M · 2021
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Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, W., Wohlwend, J., Barzilay, R., and Jaakkola, T · 2021
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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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Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., and Rives, A · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al · 2021
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Deciphering antibody affinity maturation with language models and weakly supervised learning
Ruffolo, J. A., Gray, J. J., and Sulam, J · 2021
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A fresh look at de novo molecular design benchmarks
Tripp, A., Simm, G. N., and Hernández-Lobato, J. M · 2021
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Turner, R., Eriksson, D., McCourt, M., Kiili, J., Laaksonen, E., Xu, Z., and Guyon, I · 2021
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Unifying likelihood-free inference with black-box sequence design and beyond
Zhang, D., Fu, J., Bengio, Y., and Courville, A · 2021
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Antbo: Towards real-world automated antibody design with combinatorial bayesian optimisation
Khan, A., Cowen-Rivers, A. I., Deik, D.-G.-X., Grosnit, A., Dreczkowski, K., Robert, P. A., Greiff, V., Tutunov, R., Bou-Ammar, D., Wang, J., et al · 2022
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Local latent space bayesian optimization over structured inputs
Maus, N., Jones, H. T., Moore, J. S., Kusner, M. J., Bradshaw, J., and Gardner, J. R · 2022
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