Benjamin Sanchez-Lengeling
Benjamin Sanchez-Lengeling
Research Scientist at Google Deepmind
Verified email at - Homepage
Cited by
Cited by
Automatic chemical design using a data-driven continuous representation of molecules
R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ...
ACS central science 4 (2), 268-276, 2018
Inverse molecular design using machine learning: Generative models for matter engineering
B Sanchez-Lengeling, A Aspuru-Guzik
Science 361 (6400), 360-365, 2018
Rational design of layered oxide materials for sodium-ion batteries
C Zhao, Q Wang, Z Yao, J Wang, B Sánchez-Lengeling, F Ding, X Qi, ...
Science 370 (6517), 708-711, 2020
Objective-reinforced generative adversarial networks (organ) for sequence generation models
GL Guimaraes, B Sanchez-Lengeling, C Outeiral, PLC Farias, ...
arXiv preprint arXiv:1705.10843, 2017
Molecular sets (MOSES): a benchmarking platform for molecular generation models
D Polykovskiy, A Zhebrak, B Sanchez-Lengeling, S Golovanov, O Tatanov, ...
Frontiers in pharmacology 11, 565644, 2020
Reinforced Adversarial Neural Computer for de Novo Molecular Design
E Putin, A Asadulaev, Y Ivanenkov, V Aladinskiy, B Sanchez-Lengeling, ...
Journal of chemical information and modeling 58 (6), 1194-1204, 2018
Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC)
B Sanchez-Lengeling, C Outeiral, GL Guimaraes, A Aspuru-Guzik
Inverse design of solid-state materials via a continuous representation
J Noh, J Kim, HS Stein, B Sanchez-Lengeling, JM Gregoire, ...
Matter 1 (5), 1370-1384, 2019
Inverse design of nanoporous crystalline reticular materials with deep generative models
Z Yao, B Sánchez-Lengeling, NS Bobbitt, BJ Bucior, SGH Kumar, ...
Nature Machine Intelligence 3 (1), 76-86, 2021
Design principles and top non-fullerene acceptor candidates for organic photovoltaics
SA Lopez, B Sanchez-Lengeling, J de Goes Soares, A Aspuru-Guzik
Joule 1 (4), 857-870, 2017
A gentle introduction to graph neural networks
B Sanchez-Lengeling, E Reif, A Pearce, AB Wiltschko
Distill 6 (9), e33, 2021
Machine learning for scent: Learning generalizable perceptual representations of small molecules
B Sanchez-Lengeling, JN Wei, BK Lee, RC Gerkin, A Aspuru-Guzik, ...
Machine Learning for Molecules NeuRIPS 2019 (arXiv:1910.10685), 2019
Materials Acceleration Platforms: On the way to autonomous experimentation
MM Flores-Leonar, LM Mejía-Mendoza, A Aguilar-Granda, ...
Current Opinion in Green and Sustainable Chemistry 25, 100370, 2020
Evaluating attribution for graph neural networks
B Sanchez-Lengeling, J Wei, B Lee, E Reif, P Wang, W Qian, ...
Advances in neural information processing systems 33, 5898-5910, 2020
A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315 000 redox reactions
A Jinich, B Sanchez-Lengeling, H Ren, R Harman, A Aspuru-Guzik
ACS central science 5 (7), 1199-1210, 2019
A Bayesian approach to predict solubility parameters
B Sanchez‐Lengeling, LM Roch, JD Perea, S Langner, CJ Brabec, ...
Advanced Theory and Simulations 2 (1), 1800069, 2019
Introducing a new potential figure of merit for evaluating microstructure stability in photovoltaic polymer-fullerene blends
JD Perea, S Langner, M Salvador, B Sanchez-Lengeling, N Li, C Zhang, ...
The Journal of Physical Chemistry C 121 (33), 18153-18161, 2017
Quantum chemical approach to estimating the thermodynamics of metabolic reactions
A Jinich, D Rappoport, I Dunn, B Sanchez-Lengeling, R Olivares-Amaya, ...
Scientific reports 4 (1), 7022, 2014
A machine learning workflow for molecular analysis: application to melting points
G Sivaraman, NE Jackson, B Sanchez-Lengeling, Á Vázquez-Mayagoitia, ...
Machine Learning: Science and Technology 1 (2), 025015, 2020
A Quantum Monte Carlo Study of the Reactions of CH with Acrolein
S Pakhira, B Sanchez-Lengeling, O Olatunji-Ojo, M Caffarel, M Frenklach, ...
The Journal of Physical Chemistry A 119 (18), 4214-4223, 2015
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