Chemical space: limits, evolution and modelling of an object bigger than our universal library
Restrepo, G · 2022
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Large-scale chemical language representations capture molecular structure and properties
Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., and Das, P · 2022
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Pre-train your loss: Easy Bayesian transfer learning with informative priors
Shwartz-Ziv, R., Goldblum, M., Souri, H., Kapoor, S., Zhu, C., LeCun, Y., and Wilson, A. G · 2022
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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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Closed-loop transfer enables AI to yield chemical knowledge
Angello, N., Friday, D., Hwang, C., Yi, S., Cheng, A., Torres-Flores, T., Jira, E., Wang, W., Aspuru-Guzik, A., Burke, M., and et al · 2023
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Riemannian Laplace approximations for Bayesian neural networks
Bergamin, F., Moreno-Muñoz, P., Hauberg, S., and Arvanitidis, G · 2023
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Unifying molecular and textual representations via multi-task language modelling
Christofidellis, D., Giannone, G., Born, J., Winther, O., Laino, T., and Manica, M · 2023
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The Enamine REAL database, 2023
Enamine Ltd · 2023
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Bayesian optimization
Garnett, R · 2023
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VirtualFlow 2.0 - the next generation drug discovery platform enabling adaptive screens of 69 billion molecules
Gorgulla, C., Nigam, A., Koop, M., Çınaroğlu, S. S., Secker, C., Haddadnia, M., Kumar, A., Malets, Y., Hasson, A., Li, M., Tang, M., Levin-Konigsberg, R., Radchenko, D., Kumar, A., Gehev, M., Aquilanti, P.-Y., Gabb, H., Alhossary, A., Wagner, G., Aspuru-Guzik, A., Moroz, Y. S., Fackeldey, K., and Arthanari, H · 2023
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From alchemist to AI chemist
Greenaway, R. L., Jelfs, K. E., Spivey, A. C., and Yaliraki, S. N · 2023
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GAUCHE: A library for Gaussian processes in chemistry
Griffiths, R.-R., Klarner, L., Moss, H. B., Ravuri, A., Truong, S., Stanton, S., Tom, G., Rankovic, B., Du, Y., Jamasb, A., Deshwal, A., Schwartz, J., Tripp, A., Kell, G., Frieder, S., Bourached, A., Chan, A., Moss, J., Guo, C., Durholt, J., Chaurasia, S., Strieth-Kalthoff, F., Lee, A. A., Cheng, B., Aspuru-Guzik, A., Schwaller, P., and Tang, J · 2023
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Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G · 2023
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What can large language models do in chemistry? A comprehensive benchmark on eight tasks
Original
Guo, T., Guo, K., Nan, B., Liang, Z., Guo, Z., Chawla, N. V., Wiest, O., and Zhang, X · 2023
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In-context learning of large language models explained as kernel regression
Original
Han, C., Wang, Z., Zhao, H., and Ji, H · 2023
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Olympus, enhanced: Benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science
Hickman, R., Parakh, P., Cheng, A., Ai, Q., Schrier, J., Aldeghi, M., and Aspuru-Guzik, A · 2023
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ChatGPT for good? on opportunities and challenges of large language models for education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al · 2023
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Promises and pitfalls of the linearized Laplace in Bayesian optimization
Kristiadi, A., Immer, A., Eschenhagen, R., and Fortuin, V · 2023
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The impact of large language models on scientific discovery: a preliminary study using GPT-4
Original
Microsoft Research AI4Science and Microsoft Azure Quantum · 2023
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GPT-4 technical report
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OpenAI · 2023
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Bayesian optimization of catalysts with in-context learning
Original
Ramos, M. C., Michtavy, S. S., Porosoff, M. D., and White, A. D · 2023
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BoChemian: Large language model embeddings for Bayesian optimization of chemical reactions
Ranković, B. and Schwaller, P · 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., Katwyk, P. V., Deac, A., Anandkumar, A., Bergen, K., Gomes, C. P., Ho, S., Kohli, P., Lasenby, J., Leskovec, J., Liu, T.-Y., Manrai, A., Marks, D., Ramsundar, B., Song, L., Sun, J., Tang, J., Velickovic, P., Welling, M., Zhang, L., Coley, C. W., Bengio, Y., and Zitnik, M · 2023
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Bayesian low-rank adaptation for large language models
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Yang, A. X., Robeyns, M., Wang, X., and Aitchison, L · 2023
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A study of Bayesian neural network surrogates for Bayesian optimization
Li, Y. L., Rudner, T. G., and Wilson, A. G · 2024
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Large language models to enhance Bayesian optimization
Liu, T., Astorga, N., Seedat, N., and van der Schaar, M · 2024
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Delocalized, asynchronous, closed-loop discovery of organic laser emitters
Strieth-Kalthoff, F., Hao, H., Rathore, V., Derasp, J., Gaudin, T., Angello, N. H., Seifrid, M., Trushina, E., Guy, M., Liu, J., and et al · 2024
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Self-driving laboratories for chemistry and materials sciences
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-Garcia, S., Rajaonson, E. M., Skreta, M., Yoshikawa, N., Corapi, S., Akkoc, G. D., Strieth-Kalthoff, F., Seifrid, M., and Aspuru-Guzik, A · 2024
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