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There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications involving biophysical data.
Adaptation in tunably rugged fitness landscapes: The rough mount fuji model
Neidhart, J., Szendro, I. G., and Krug, J · 1943
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Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Back, T · 1996
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Casp and cafasp experiments and their findings
Bourne, P. E · 2003
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Population genetics: a concise guide
Gillespie, J. H · 2004
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Test functions for optimization needs
Molga, M. and Smutnicki, C · 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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Uniref: comprehensive and non-redundant uniprot reference clusters
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R., and Wu, C. H · 2007
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Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Chaudhury, S., Lyskov, S., and Gray, J. J · 2010
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Role of conformational sampling in computing mutation-induced changes in protein structure and stability
Kellogg, E. H., Leaver-Fay, A., and Baker, D · 2011
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The importance of the ising model
McCoy, B. M. and Maillard, J.-M · 2012
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Survey of variation in human transcription factors reveals prevalent dna binding changes
Barrera, L. A., Vedenko, A., Kurland, J. V., Rogers, J. M., Gisselbrecht, S. S., Rossin, E. J., Woodard, J., Mariani, L., Kock, K. H., Inukai, S., et al · 2016
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Ten years of wmt evaluation campaigns: Lessons learnt
Bojar, O., Federmann, C., Haddow, B., Koehn, P., Post, M., and Specia, L · 2016
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Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S., Bolotin, D. A., Meer, M. V., Usmanova, D. R., Mishin, A. S., Sharonov, G. V., Ivankov, D. N., Bozhanova, N. G., Baranov, M. S., Soylemez, O., et al · 2016
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Adaptation in protein fitness landscapes is facilitated by indirect paths
Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., and Sun, R · 2016
Cited alongside, same era.
Flex ddg: Rosetta ensemble-based estimation of changes in protein–protein binding affinity upon mutation
Barlow, K. A., O Conchuir, S., Thompson, S., Suresh, P., Lucas, J. E., Heinonen, M., and Kortemme, T · 2018
Cited alongside, same era.
Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
Cited alongside, same era.
Comprehensive aav capsid fitness landscape reveals a viral gene and enables machine-guided design
Ogden, P. J., Kelsic, E. D., Sinai, S., and Church, G. M · 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.
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A., Bonati, L., Dahinden, J., Gainza, P., et al · 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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Accelerating bayesian optimization for biological sequence design with denoising autoencoders
Stanton, S., Maddox, W., Gruver, N., Maffettone, P., Delaney, E., Greenside, P., and Wilson, A. G · 2022
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Language models generalize beyond natural proteins
Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., and Rives, A · 2022
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Scaffolding protein functional sites using deep learning
Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J. L., Castro, K. M., Ragotte, R., Saragovi, A., Milles, L. F., Baek, M., et al · 2022
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Evaluating protein transfer learning with tape
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., and Song, Y. S · 2019
Cited alongside, same era.
Population-based black-box optimization for biological sequence design
Angermueller, C., Belanger, D., Gane, A., Mariet, Z., Dohan, D., Murphy, K., Colwell, L., and Sculley, D · 2020
Cited alongside, same era.
Botorch: A framework for efficient monte-carlo bayesian optimization
Balandat, M., Karrer, B., Jiang, D., Daulton, S., Letham, B., Wilson, A. G., and Bakshy, E · 2020
Cited alongside, same era.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Cited alongside, same era.
Adalead: A simple and robust adaptive greedy search algorithm for sequence design
Sinai, S., Wang, R., Whatley, A., Slocum, S., Locane, E., and Kelsic, E · 2020
Cited alongside, same era.
Function-guided protein design by deep manifold sampling
Gligorijević, V., Berenberg, D., Ra, S., Watkins, A., Kelow, S., Cho, K., and Bonneau, R · 2021
Cited alongside, same era.
Accelerating high-throughput virtual screening through molecular pool-based active learning
Graff, D. E., Shakhnovich, E. I., and Coley, C. W · 2021
Cited alongside, same era.
Peer: a comprehensive and multi-task benchmark for protein sequence understanding
Xu, M., Zhang, Z., Lu, J., Zhu, Z., Zhang, Y., Chang, M., Liu, R., and Tang, J · 2022
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Generative models should at least be able to design molecules that dock well: A new benchmark
Cieplinski, T., Danel, T., Podlewska, S., and Jastrzebski, S · 2023
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Investigating the volume and diversity of data needed for generalizable antibody-antigen δ \delta δ \delta g prediction
Hummer, A. M., Schneider, C., Chinery, L., and Deane, C. M · 2023
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Proteingym: Large-scale benchmarks for protein fitness prediction and design
Notin, P., Kollasch, A., Ritter, D., van Niekerk, L., Paul, S., Spinner, H., Rollins, N., Shaw, A., Orenbuch, R., Weitzman, R., Frazer, J., Dias, M., Franceschi, D., Gal, Y., and Marks, D · 2023
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Accurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models
Ooka, K. and Arai, M · 2023
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Baselining the buzz. trastuzumab-her2 affinity, and beyond!
Chinery, L., Hummer, A. M., Mehta, B. B., Akbar, R., Rawat, P., Slabodkin, A., Le Quy, K., Lund-Johansen, F., Greiff, V., Jeliazkov, J. R., et al · 2024
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Protein design with guided discrete diffusion
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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Efficient evolution of human antibodies from general protein language models
Hie, B. L., Shanker, V. R., Xu, D., Bruun, T. U., Weidenbacher, P. A., Tang, S., Wu, W., Pak, J. E., and Kim, P. S · 2024
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