Benjamín Sánchez-Lengeling

发表

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, C. Outeiral, 2017 .

A Bayesian Approach to Predict Solubility Parameters

C. Brabec, A. Aspuru‐Guzik, Benjamín Sánchez-Lengeling, 2018, Advanced Theory and Simulations.

The Chemistry of Smell: Learning Generalizable Perceptual Representations of Small Molecules

Alexander B. Wiltschko, Brian K. Lee, Jennifer N. Wei, 2019 .

Quantum chemistry reveals thermodynamic principles of redox biochemistry

Alán Aspuru-Guzik, Sung-Jin Kim, Elad Noor, 2018, PLoS Comput. Biol..

The thermodynamic landscape of carbon redox biochemistry

D. Segrè, J. N. Sanders, A. Aspuru‐Guzik, 2020 .

A thermodynamic atlas of carbon redox chemical space

Alán Aspuru-Guzik, D. Segrè, J. N. Sanders, 2019, Proceedings of the National Academy of Sciences of the United States of America.

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

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, A. Jinich, 2018, bioRxiv.

Quantum Chemical Approach to Estimating the Thermodynamics of Metabolic Reactions

Alán Aspuru-Guzik, Dmitrij Rappoport, Benjamín Sánchez-Lengeling, 2014, Scientific Reports.

Quantum chemistry reveals the thermodynamic principles of redox biochemistry

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, E. Noor, 2018, bioRxiv.

A Diversified Machine Learning Strategy for Predicting and Understanding Molecular Melting Points

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, Á. Vázquez-Mayagoitia, 2019 .

A machine learning workflow for molecular analysis: application to melting points

Alán Aspuru-Guzik, Juan J. de Pablo, Venkatram Vishwanath, 2020, Mach. Learn. Sci. Technol..

Inverse design of nanoporous crystalline reticular materials with deep generative models

Alán Aspuru-Guzik, Randall Q. Snurr, Tom K. Woo, 2021, Nat. Mach. Intell..

Materials Acceleration Platforms: On the way to autonomous experimentation

Alán Aspuru-Guzik, Benjamin Sanchez-Lengeling, Hermann Tribukait, 2020 .

Metabolic activity organizes olfactory representations

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2022, bioRxiv.

Metabolic activity organizes olfactory representations

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2022, bioRxiv.

A deep learning and digital archaeology approach for mosquito repellent discovery

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2022, bioRxiv.

A deep learning and digital archaeology approach for mosquito repellent discovery

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2022, bioRxiv.

A Bayesian Approach to Predict Solubility Parameters

C. Brabec, A. Aspuru‐Guzik, Benjamín Sánchez-Lengeling, 2018, Advanced Theory and Simulations.

A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2022, bioRxiv.

Introducing a New Potential Figure of Merit for Evaluating Microstructure Stability in Photovoltaic Polymer-Fullerene Blends

Christoph J. Brabec, Michael Salvador, Benjamin Sanchez-Lengeling, 2017 .

A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2022, bioRxiv.

Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics

Alán Aspuru-Guzik, Benjamin Sanchez-Lengeling, Alán Aspuru-Guzik, 2017 .

Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

Riley J. Hickman, A. Aspuru‐Guzik, Benjamín Sánchez-Lengeling, 2022, 2212.01574.

A deep learning and digital archaeology approach for mosquito repellent discovery

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2024, bioRxiv.

Inverse molecular design using machine learning: Generative models for matter engineering

Alán Aspuru-Guzik, Benjamin Sanchez-Lengeling, Alán Aspuru-Guzik, 2018, Science.

Rational design of layered oxide materials for sodium-ion batteries

Chenglong Zhao, Xingguo Qi, Yaxiang Lu, 2020, Science.

Learning More, with Less

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, Alán Aspuru-Guzik, 2017, ACS central science.

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Alán Aspuru-Guzik, Sergey I. Nikolenko, Alex Zhavoronkov, 2018, Frontiers in Pharmacology.

Coronene derivatives for transparent organic photovoltaics through inverse materials design

Nicholas C. Davy, Alán Aspuru-Guzik, Pascal Friederich, 2021, Journal of Materials Chemistry C.

Inverse Design of Solid-State Materials via a Continuous Representation

Alán Aspuru-Guzik, John M. Gregoire, Jae-Hoon Kim, 2019, Matter.

Inverse design of nanoporous crystalline reticular materials with deep generative models

Benjamin J. Bucior, Sean P. Collins, Alán Aspuru-Guzik, 2020, Nature Machine Intelligence.

Inverse design of nanoporous crystalline reticular materials with deep generative models

Benjamin J. Bucior, Sean P. Collins, Alán Aspuru-Guzik, 2021, Nature Machine Intelligence.

Reinforced Adversarial Neural Computer for de Novo Molecular Design

Alán Aspuru-Guzik, Evgeny Putin, Benjamin Sanchez-Lengeling, 2018, J. Chem. Inf. Model..

Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models pdf

Alán Aspuru-Guzik, Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, 2017, ArXiv.

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, C. Outeiral, 2017 .

Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules pdf

Richard C. Gerkin, Alexander B. Wiltschko, Al'an Aspuru-Guzik, 2019, ArXiv.

A Gentle Introduction to Graph Neural Networks

Emily Reif, Alexander B. Wiltschko, Benjamin Sanchez-Lengeling, 2021, Distill.

Evaluating Attribution for Graph Neural Networks

Lucy J. Colwell, Emily Reif, Alexander B. Wiltschko, 2020, NeurIPS.

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

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, A. Jinich, 2019, ACS central science.

Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

Riley J. Hickman, A. Aspuru‐Guzik, Benjamín Sánchez-Lengeling, 2022, Digital Discovery.

A thermodynamic atlas of carbon redox chemical space

Alán Aspuru-Guzik, D. Segrè, J. N. Sanders, 2019, Proceedings of the National Academy of Sciences.

Metabolic activity organizes olfactory representations

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2022, bioRxiv.

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

Alán Aspuru-Guzik, Benjamín Sánchez-Lengeling, A. Jinich, 2018, bioRxiv.

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Alán Aspuru-Guzik, A. Zhavoronkov, S. Nikolenko, 2018, Frontiers in Pharmacology.

Delocalized, asynchronous, closed-loop discovery of organic laser emitters

Riley J. Hickman, Ralph C. Sigerson, Cher Tian Ser, 2024, Science.

A principal odor map unifies diverse tasks in olfactory perception

Alexander B. Wiltschko, Wesley Wei Qian, Brian K. Lee, 2023, Science.