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We introduce GAUCHE, a library for GAUssian processes in CHEmistry.
A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
Kushner, H. J · 1963
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A general coefficient of similarity and some of its properties
Gower, J. C · 1971
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On Bayesian methods for seeking the extremum
Močkus, J · 1975
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Single-step Bayesian search method for an extremum of functions of a single variable
Zhilinskas, A · 1975
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SMILES, a line notation and computerized interpreter for chemical structures
Anderson, E., Veith, G. D., and Weininger, D · 1987
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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 limited memory BFGS method for large scale optimization
Liu, D. C. and Nocedal, J · 1989
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Concepts and applications of molecular similarity
Johnson, M. A. and Maggiora, G. M · 1990
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Structure searching in chemical databases by direct lookup methods
Christie, B. D., Leland, B. A., and Nourse, J. G · 1993
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Clustering of large databases of compounds: using the MDL “keys” as structural descriptors
McGregor, M. J. and Pallai, P. V · 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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An introduction to natural language processing, computational linguistics, and speech recognition, 2000
Jurafsky, D. and Martin, J. H · 2000
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Occam’s razor
Rasmussen, C. E. and Ghahramani, Z · 2001
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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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Word sequence kernels
Cancedda, N., Gaussier, E., Goutte, C., and Renders, J. M · 2003
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Information theory, inference and learning algorithms
MacKay, D. J., Mac Kay, D. J., et al · 2003
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ESOL: Estimating aqueous solubility directly from molecular structure
Delaney, J. S · 2004
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Classification of kinase inhibitors using a Bayesian model
Xia, X., Maliski, E. G., Gallant, P., and Rogers, D · 2004
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Graph kernels for chemical informatics
Ralaivola, L., Swamidass, S. J., Saigo, H., and Baldi, P · 2005
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Cheminformatics analysis and learning in a data pipelining environment
Hassan, M., Brown, R. D., Varma-O’Brien, S., and Rogers, D · 2006
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Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
Knowles, J · 2006
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Variational learning of inducing variables in sparse gaussian processes
Titsias, M · 2009
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Brochu, E., Cora, V. M., and De Freitas, N · 2010
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M · 2010
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Graph kernels
Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., and Borgwardt, K. M · 2010
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What are some Advantages of Using Gaussian Process Models vs Neural Networks? , 2011
Bengio, Y · 2011
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Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., Van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M · 2011
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In silico toxicity prediction by support vector machine and SMILES representation-based string kernel
Cao, D.-S., Zhao, J.-C., Yang, Y.-N., Zhao, C.-X., Yan, J., Liu, S., Hu, Q.-N., Xu, Q.-S., and Liang, Y.-Z · 2012
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ChEMBL: A large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al · 2012
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Graph kernels based on relevant patterns and cycle information for chemoinformatics
Gaüzére, B., Brun, L., Villemin, D., and Brun, M · 2012
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GPy: A Gaussian process framework in Python
GPy · 2012
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Entropy search for information-efficient global optimization
Hennig, P. and Schuler, C. J · 2012
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On representing chemical environments
Bartók, A. P., Kondor, R., and Csányi, G · 2013
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Gaussian processes for big data
Hensman, J., Fusi, N., and Lawrence, N. D · 2013
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RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Landrum, G · 2013
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Multi-task Bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P · 2013
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The ChEMBL bioactivity database: An update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al · 2014
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Predictive entropy search for efficient global optimization of black-box functions
Hernández-Lobato, J. M., Hoffman, M. W., and Ghahramani, Z · 2014
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FreeSolv: A database of experimental and calculated hydration free energies, with input files
Mobley, D. L. and Guthrie, J. P · 2014
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Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
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What is high-throughput virtual screening? A perspective from organic materials discovery
Pyzer-Knapp, E. O., Suh, C., Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., and Aspuru-Guzik, A · 2015
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Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity
Schneider, N., Lowe, D. M., Sayle, R. A., and Landrum, G. A · 2015
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Scalable Bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R · 2015
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Halting in random walk kernels
Sugiyama, M. and Borgwardt, K · 2015
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Batch Bayesian optimization via local penalization
González, J., Dai, Z., Hennig, P., and Lawrence, N · 2016
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Molecular graph convolutions: Moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016
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Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N · 2016
Pyzer-Knapp, E. O · 2020
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A structure-based platform for predicting chemical reactivity
Sandfort, F., Strieth-Kalthoff, F., Kühnemund, M., Beecks, C., and Glorius, F · 2020
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Evaluating scalable uncertainty estimation methods for deep learning-based molecular property prediction
Scalia, G., Grambow, C. A., Pernici, B., Li, Y.-P., and Green, W. H · 2020
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Grakel: A graph kernel library in Python
Siglidis, G., Nikolentzos, G., Limnios, S., Giatsidis, C., Skianis, K., and Vazirgiannis, M · 2020
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Bayesian quantile and expectile optimisation
Torossian, L., Picheny, V., and Durrande, N · 2020
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Bayesian optimization with robust Bayesian neural networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F · 2016
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Deep kernel learning
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P · 2016
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Classification and regression trees
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J · 2017
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Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space
Hernández-Lobato, J. M., Requeima, J., Pyzer-Knapp, E. O., and Aspuru-Guzik, A · 2017
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GPflow: A Gaussian process library using TensorFlow
Matthews, A. G., van der Wilk, M., Nickson, T., Fujii, K., Boukouvalas, A., León-Villagrá, P., Ghahramani, Z., and Hensman, J · 2017
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Max-value entropy search for efficient Bayesian optimization
Wang, Z. and Jegelka, S · 2017
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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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A framework for interdomain and multioutput Gaussian processes
van der Wilk, M., Dutordoir, V., John, S., Artemev, A., Adam, V., and Hensman, J · 2020
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Applications of deep learning in molecule generation and molecular property prediction
Walters, W. P. and Barzilay, R · 2020
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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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Summit: Benchmarking machine learning methods for reaction optimisation
Felton, K. C., Rittig, J. G., and Lapkin, A. A · 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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Kernel methods for predicting yields of chemical reactions
Haywood, A. L., Redshaw, J., Hanson-Heine, M. W., Taylor, A., Brown, A., Mason, A. M., Gärtner, T., and Hirst, J. D · 2021
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What are bayesian neural network posteriors really like?
Izmailov, P., Vikram, S., Hoffman, M. D., and Wilson, A. G. G · 2021
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Bias free multiobjective active learning for materials design and discovery
Jablonka, K. M., Jothiappan, G. M., Wang, S., Smit, B., and Yoo, B · 2021
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Graphein - a Python library for geometric deep learning and network analysis on protein structures and interaction networks
Jamasb, A. R., Viñas, R., Ma, E. J., Harris, C., Huang, K., Hall, D., Lió, P., and Blundell, T. L · 2021
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graphkit-learn: A Python library for graph kernels based on linear patterns
Jia, L., Gaüzère, B., and Honeine, P · 2021
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DGL-LifeSci: An open-source toolkit for deep learning on graphs in life science
Li, M., Zhou, J., Hu, J., Fan, W., Zhang, Y., Gu, Y., and Karypis, G · 2021
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Conditioning sparse variational gaussian processes for online decision-making
Maddox, W. J., Stanton, S., and Wilson, A. G · 2021
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Gibbon: General-purpose information-based Bayesian optimisation
Moss, H. B., Leslie, D. S., Gonzalez, J., and Rayson, P · 2021
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Graph kernels: A survey
Nikolentzos, G., Siglidis, G., and Vazirgiannis, M · 2021
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Bayesian reaction optimization as a tool for chemical synthesis
Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., Janey, J. M., Adams, R. P., and Doyle, A. G · 2021
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Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
Turner, R., Eriksson, D., McCourt, M., Kiili, J., Laaksonen, E., Xu, Z., and Guyon, I · 2021
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Scalable Thompson sampling using sparse Gaussian process models
Vakili, S., Moss, H., Artemev, A., Dutordoir, V., and Picheny, V · 2021
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Uncertainty-aware labelled augmentations for high dimensional latent space Bayesian optimization
Verma, E. and Chakraborty, S · 2021
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Fantasizing with dual gps in bayesian optimization and active learning
Chang, P. E., Verma, P., John, S., Picheny, V., Moss, H., and Solin, A · 2022
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Molgensurvey: A systematic survey in machine learning models for molecule design
Du, Y., Fu, T., Sun, J., and Liu, S · 2022
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Self-focusing virtual screening with active design space pruning
Graff, D. E., Aldeghi, M., Morrone, J. A., Jordan, K. E., Pyzer-Knapp, E. O., and Coley, C. W · 2022
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Data-driven discovery of molecular photoswitches with multioutput Gaussian processes
Griffiths, R.-R., Greenfield, J. L., Thawani, A. R., Jamasb, A. R., Moss, H. B., Bourached, A., Jones, P., McCorkindale, W., Aldrick, A. A., Fuchter, M. J., et al · 2022
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Equipping data-driven experiment planning for self-driving laboratories with semantic memory: Case studies of transfer learning in chemical reaction optimization
Hickman, R., Ruža, J., Roch, L., Tribukait, H., and García-Durán, A · 2022
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Bayesian-torch: Bayesian neural network layers for uncertainty estimation
Krishnan, R., Esposito, P., and Subedar, M · 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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Information-theoretic inducing point placement for high-throughput bayesian optimisation
Moss, H. B., Ober, S. W., and Picheny, V · 2022
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Automated multi-objective reaction optimisation: Which algorithm should I use?
Müller, P., Clayton, A. D., Manson, J., Riley, S., May, O. S., Govan, N., Notman, S., Ley, S. V., Chamberlain, T. W., and Bourne, R. A · 2022
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The effect of chemical representation on active machine learning towards closed-loop optimization
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