1 code implementation • 3 Dec 2022 • Gary Tom, Riley J. Hickman, Aniket Zinzuwadia, Afshan Mohajeri, Benjamin Sanchez-Lengeling, Alan Aspuru-Guzik
Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties.
1 code implementation • 29 Mar 2022 • Riley J. Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru-Guzik
The tools developed constitute a simple, yet versatile strategy to enable model-based optimization with known experimental constraints, contributing to its applicability as a core component of autonomous platforms for scientific discovery.
1 code implementation • 5 Mar 2021 • Riley J. Hickman, Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik
We recommend using Gemini for regression tasks with sparse data and in an autonomous workflow setting where its predictions of expensive to evaluate objectives can be used to construct a more informative acquisition function, thus reducing the number of expensive evaluations an optimizer needs to achieve desired target values.
1 code implementation • 5 Mar 2021 • Matteo Aldeghi, Florian Häse, Riley J. Hickman, Isaac Tamblyn, Alán Aspuru-Guzik
Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently.
1 code implementation • 23 Feb 2021 • AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley, Riley J. Hickman, Matteo Aldeghi, Naruki Yoshikawa, Seyone Chithrananda, Vincent A. Voelz, Alán Aspuru-Guzik
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.
1 code implementation • 8 Oct 2020 • Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, Alán Aspuru-Guzik
Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials.
no code implementations • 26 Mar 2020 • Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Alán Aspuru-Guzik
Leveraging domain knowledge in the form of physicochemical descriptors, Gryffin can significantly accelerate the search for promising molecules and materials.