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Model-based optimization (MBO) is increasingly applied to design problems in science and engineering.
Applied Nonlinear Programming
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F. H. Arnold · 1998
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Eligibility traces for off-policy policy evaluation
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Improving predictive inference under covariate shift by weighting the log-likelihood function
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Trp-cage: Folding free energy landscape in explicit water
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Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
M. Sugiyama, S. Nakajima, H. Kashima, P. Buenau, and M. Kawanabe · 2007
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Statistical outlier detection using direct density ratio estimation
S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, and T. Kanamori · 2011
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Practical Bayesian Optimization of Machine Learning Algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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Density Ratio Estimation in Machine Learning
M. Sugiyama, T. Suzuki, and T. Kanamori · 2012
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2018
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Unified rational protein engineering with sequence-based deep representation learning
E. C. Alley, G. Khimulya, S. Biswas, M. AlQuraishi, and G. M. Church · 2019
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Model-based reinforcement learning for biological sequence design
C. Angermueller, D. Dohan, D. Belanger, R. Deshpande, K. Murphy, and L. Colwell · 2019
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Conditioning by adaptive sampling for robust design
D. H. Brookes, H. Park, and J. Listgarten · 2019
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Feedback GAN for DNA optimizes protein functions
A. Gupta and J. Zou · 2019
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Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design
P. J. Ogden, E. D. Kelsic, S. Sinai, and G. M. Church · 2019
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Learning the pattern of epistasis linking genotype and phenotype in a protein
F. J. Poelwijk, M. Socolich, and R. Ranganathan · 2019
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Machine learning-assisted directed protein evolution with combinatorial libraries
Z. Wu, S. J. Kan, R. D. Lewis, B. J. Wittmann, and F. H. Arnold · 2019
Highly accurate protein structure prediction with AlphaFold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis · 2021
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Transformer-based protein generation with regularized latent space optimization
E. Castro, A. Godavarthi, J. Rubinfien, K. Givechian, D. Bhaskar, and S. Krishnaswamy · 2022
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Conformal prediction under feedback covariate shift for biomolecular design
C. Fannjiang, S. Bates, A. N. Angelopoulos, J. Listgarten, and M. I. Jordan · 2022
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Progen2: exploring the boundaries of protein language models
E. Nijkamp, J. Ruffolo, E. N. Weinstein, N. Naik, and A. Madani · 2022
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Design-bench: Benchmarks for data-driven offline model-based optimization
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Machine-learning-guided directed evolution for protein engineering
K. K. Yang, Z. Wu, and F. H. Arnold · 2019
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Autofocused oracles for model-based design
C. Fannjiang and J. Listgarten · 2020
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A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences
J. Linder, N. Bogard, A. B. Rosenberg, and G. Seelig · 2020
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A primer on model-guided exploration of fitness landscapes for biological sequence design
S. Sinai and E. D. Kelsic · 2020
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Adalead: A simple and robust adaptive greedy search algorithm for sequence design, 2020
S. Sinai, R. Wang, A. Whatley, S. Slocum, E. Locane, and E. D. Kelsic · 2020
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Deep diversification of an AAV capsid protein by machine learning
D. H. Bryant, A. Bashir, S. Sinai, N. K. Jain, P. J. Ogden, P. F. Riley, G. M. Church, L. J. Colwell, and E. D. Kelsic · 2021
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B. Trabucco, X. Geng, A. Kumar, and S. Levine · 2022
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Non-identifiability and the blessings of misspecification in models of molecular fitness
E. Weinstein, A. Amin, J. Frazer, and D. Marks · 2022
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Forecasting labels under distribution-shift for machine-guided sequence design
L. B. Wheelock, S. Malina, J. Gerold, and S. Sinai · 2022
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C. Fannjiang and J. Listgarten · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, N. Smetanin, R. Verkuil, O. Kabeli, Y. Shmueli, A. dos Santos Costa, M. Fazel-Zarandi, T. Sercu, S. Candido, and A. Rives · 2023
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Large language models generate functional protein sequences across diverse families
A. Madani, B. Krause, E. R. Greene, S. Subramanian, B. P. Mohr, J. M. Holton, J. L. Olmos Jr, C. Xiong, Z. Z. Sun, R. Socher, et al · 2023
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Estimating the density ratio between distributions with high discrepancy using multinomial logistic regression
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Bayesian optimization with conformal prediction sets
S. Stanton, W. Maddox, and A. G. Wilson · 2023
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Scientific discovery in the age of artificial intelligence
H. Wang, T. Fu, Y. Du, W. Gao, K. Huang, Z. Liu, P. Chandak, S. Liu, P. Van Katwyk, A. Deac, et al · 2023
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