Alán Aspuru-Guzik
University of Toronto
664 Papers
4.7K Citations
Alán Aspuru-Guzik is an academic researcher from University of Toronto. The author has contributed to research in topics: Quantum computer & Quantum. The author has an hindex of 97, co-authored 628 publications. Previous affiliations of Alán Aspuru-Guzik include D-Wave Systems & National Autonomous University of Mexico.
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Papers
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
Fengzhi Ren,Xiao Ding,Min Zheng,Mikhail Korzinkin,Xin Cai,Wei Zhu,Alexey B. Mantsyzov,Alexander Aliper,Vladimir A. Aladinskiy,Zhongying Cao,Shanshan Kong,Xi Long,Bonnie Hei Man Liu,Yingtao Liu,Vladimir Naumov,Anastasia Shneyderman,Ivan V. Ozerov,Ju Wang,Frank Wing Pun,Daniil Polykovskiy,Chong Sun,Michael Levitt,Alán Aspuru-Guzik,Alex Zhavoronkov +23 more
TL;DR: In this paper , the AlphaFold algorithm was used to predict protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology.
Exponentially More Precise Quantum Simulation of Fermions in Second Quantization
Ryan Babbush,Ryan Babbush,Dominic W. Berry,Ian D. Kivlichan,Annie Yuan Wei,Peter J. Love,Alán Aspuru-Guzik +6 more
TL;DR: In this paper, a truncated Taylor series is used to obtain logarithmic scaling with the inverse of the desired precision for sparse Hamiltonian simulation of fermionic systems.
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Mapping the frontiers of quinone stability in aqueous media: implications for organic aqueous redox flow batteries
Daniel P. Tabor,Rafael Gómez-Bombarelli,Liuchuan Tong,Roy G. Gordon,Michael J. Aziz,Alán Aspuru-Guzik,Alán Aspuru-Guzik +6 more
TL;DR: In this article, the authors studied two mechanisms of nucleophilic addition of water, one reversible and one irreversible, that limit quinone performance in practical flow batteries and quantified the source of the instability of quinones in water, and explored the relationships between chemical structure, electrochemical reduction potential, and decomposition or instability mechanisms.
168
Data-science driven autonomous process optimization
Melodie Christensen,Melodie Christensen,Lars P. E. Yunker,Folarin Adedeji,Florian Häse,Loïc M. Roch,Tobias Gensch,Gabriel dos Passos Gomes,Tara Zepel,Matthew S. Sigman,Alán Aspuru-Guzik,Jason E. Hein +11 more
TL;DR: A closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times is developed, and it is found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization.
Machine learning directed drug formulation development.
TL;DR: In this paper, the authors introduce the basic concepts of ML-directed workflows and discuss how these tools can be used to aid in the development of various types of drug formulations.
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