Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Riley J. Hickman"'
Autor:
Pauric Bannigan, Zeqing Bao, Riley J. Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru-Guzik, Christine Allen
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)
Polymer-based long-acting injectable drugs are a promising therapeutic strategy for chronic diseases. Here the authors use machine learning to inform the data-driven development of advanced drug formulations.
Externí odkaz:
https://doaj.org/article/256dcc5f7458463f829f40bd7b6f49fa
Autor:
Martin Seifrid, Riley J. Hickman, Andrés Aguilar-Granda, Cyrille Lavigne, Jenya Vestfrid, Tony C. Wu, Théophile Gaudin, Emily J. Hopkins, Alán Aspuru-Guzik
Publikováno v:
ACS Central Science, Vol 8, Iss 1, Pp 122-131 (2022)
Externí odkaz:
https://doaj.org/article/c84f6135411e4d8c9bab261cf2fe45bf
Publikováno v:
Digital Discovery. 1:732-744
Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design of experiment. When combined with automated laboratory hardware and h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0d96ab3d81ea248729575bdf6f7a90b
https://doi.org/10.1039/d2dd00028h
https://doi.org/10.1039/d2dd00028h
Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle techno
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84637d0d5e406fdc4b03e1f64a903a12
https://doi.org/10.26434/chemrxiv-2022-w6s0g
https://doi.org/10.26434/chemrxiv-2022-w6s0g
A molecular computing approach to solving optimization problems via programmable microdroplet arrays
Autor:
Douglas Mendoza, Alán Aspuru-Guzik, Nathan C. Gianneschi, Veronica K. Krasecki, Andrew C. Cavell, Pascal Friederich, Matthias Degroote, Si Yue Guo, Leroy Cronin, Chris Forman, Randall H. Goldsmith, Yudong Cao, Tony C. Wu, Riley J. Hickman, Abhishek Sharma
Publikováno v:
Matter. 4:1107-1124
Summary The search for novel forms of computing to the dominant von Neumann model-based approach is important as it will enable different classes of problems to be solved. Molecular computers are a promising alternative to semiconductor-based compute
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ca0a967f1cdc7bca6b34fd1a67e0a3fe
https://doi.org/10.1016/j.matt.2021.03.002
https://doi.org/10.1016/j.matt.2021.03.002
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Autor:
Gary Tom, Riley J. Hickman, Aniket Zinzuwadia, Afshan Mohajeri, Benjamin Sanchez-Lengeling, Alán Aspuru-Guzik
Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties. When the datasets are smaller (< 2000 molecules), it is not clear that deep learning approaches are the right modelling tool. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e045fac8d666b2b68a4771c2e0d67a9
Publikováno v:
Chemical Science
Numerous challenges in science and engineering can be framed as optimization tasks, including the maximization of reaction yields, the optimization of molecular and materials properties, and the fine-tuning of automated hardware protocols. Design of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6e1ddf9e702a1fdcb95cbaa25e90738
https://nrc-publications.canada.ca/eng/view/object/?id=7462f814-06a2-400c-922d-44f8d6fef347
https://nrc-publications.canada.ca/eng/view/object/?id=7462f814-06a2-400c-922d-44f8d6fef347
Autor:
Théophile Gaudin, Riley J. Hickman, Jenya Vestfrid, Cyrille Lavigne, Martin Seifrid, Emily J. Hopkins, Andrés Aguilar-Granda, Alán Aspuru-Guzik, Tony C. Wu
Self-driving labs, in the form of automated experimentation platforms guided by machine learning algorithms have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are bein
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6269d24a275054d67fdcb6ba05236b6f
https://doi.org/10.33774/chemrxiv-2021-k0qx5
https://doi.org/10.33774/chemrxiv-2021-k0qx5
Autor:
AkshatKumar Nigam, Seyone Chithrananda, Riley J. Hickman, Alán Aspuru-Guzik, Matteo Aldeghi, Naruki Yoshikawa, Matthew F. D. Hurley, Vincent A. Voelz, Robert Pollice
Publikováno v:
Expert Opin Drug Discov
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recently, machine learning techniques have emerged as a powerful and cost-eff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2a18278e6f0a6905a98229d61cccb40
https://pubmed.ncbi.nlm.nih.gov/34126827
https://pubmed.ncbi.nlm.nih.gov/34126827
Autor:
Zhenpeng Yao, Robert Pollice, Gabriel dos Passos Gomes, Cyrille Lavigne, Matteo Aldeghi, Mario Krenn, Michael Lindner-D’Addario, Cher Tian Ser, AkshatKumar Nigam, Riley J. Hickman, Alán Aspuru-Guzik
Publikováno v:
Accounts of Chemical Research. AMER CHEMICAL SOC INC
Accounts of Chemical Research
Accounts of Chemical Research
Conspectus The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca2068ecee8ef784a8e82b5aea6b51fe
https://research.rug.nl/en/publications/f05c327c-8228-444b-85f7-92ba359d50c3
https://research.rug.nl/en/publications/f05c327c-8228-444b-85f7-92ba359d50c3