Papers citing Inverse molecular design using machine learning: Generative models for matter engineering
Preface
Annual Review of Physical Chemistry · 1987
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Annual Review of Physical Chemistry · 2011
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Annual Review of Physical Chemistry · 2012
Mark A. Johnson, Todd J. Martı́nez
Read original paper ↗ ·Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
Nanophotonics · 2018
Kan Yao, Rohit Unni, Yuebing Zheng
Read original paper ↗ ·Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
Molecular Pharmaceutics · 2018
Daniil A. Polykovskiy, Alexander Zhebrak, Dmitry Vetrov, Yan A. Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine E. Bozdaganyan, Alexander M. Aliper, Alex A. Zhavoronkov, Artur Kadurin
Read original paper ↗ ·Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry
Molecular Pharmaceutics · 2018
Alex A. Zhavoronkov
Read original paper ↗ ·Transforming Computational Drug Discovery with Machine Learning and AI
ACS Medicinal Chemistry Letters · 2018
Justin S. Smith, Adrián E. Roitberg, Olexandr Isayev
Read original paper ↗ ·A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians
Journal of Chemical Theory and Computation · 2018
Haichen Li, Christopher R. Collins, Matteus Tanha, Geoffrey J. Gordon, David Yaron
Read original paper ↗ ·Machine learning of optical properties of materials – predicting spectra from images and images from spectra
Chemical Science · 2018
Helge Sören Stein, Dan Guevarra, Paul F. Newhouse, Edwin Soedarmadji, John M. Gregoire
Read original paper ↗ ·Read between the Molecules: Computational Insights into Organic Semiconductors
Journal of the American Chemical Society · 2018
Ganna Gryn’ova, Kun‐Han Lin, Clémence Corminbœuf
Read original paper ↗ ·Deep Learning for Nonadiabatic Excited-State Dynamics
The Journal of Physical Chemistry Letters · 2018
Wenkai Chen, Xiangyang Liu, Wei‐Hai Fang, Pavlo O. Dral, Ganglong Cui
Read original paper ↗ ·Synthesis of Lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening
Chemistry Letters · 2018
Kan Hatakeyama‐Sato, Toshiki Tezuka, Yoshinori Nishikitani, Hiroyuki Nishide, Kenichi Oyaizu
Read original paper ↗ ·Inverse Design of a Catalyst for Aqueous CO/CO2 Conversion Informed by the NiII–Iminothiolate Complex
Inorganic Chemistry · 2018
Alexander M. Chang, Benjamin Rudshteyn, Ingolf Warnke, Víctor S. Batista
Read original paper ↗ ·The Bicyclo[2.2.2]octane Motif: A Class of Saturated Group 14 Quantum Interference Based Single-Molecule Insulators
The Journal of Physical Chemistry Letters · 2018
Marc H. Garner, Mads Koerstz, Jan Halborg Jensen, Gemma C. Solomon
Read original paper ↗ ·Closed-loop discovery platform integration is needed for artificial intelligence to make an impact in drug discovery
Expert Opinion on Drug Discovery · 2018
Semion K. Saikin, Christoph Kreisbeck, Dennis Sheberla, Jill S. Becker, Alán Aspuru‐Guzik
Read original paper ↗ ·Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests
bioRxiv (Cold Spring Harbor Laboratory) · 2018
Jesse G. Meyer, Shengchao Liu, Ian J. Miller, Joshua J. Coon, Anthony Gitter
Read original paper ↗ ·Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
arXiv (Cornell University) · 2018
Pope, Phillip, Soheil Kolouri, Rostrami, Mohammad, Charles E. Martin, Heiko Hoffmann
Read original paper ↗ ·Machine Learning Approaches for Process Optimization
IEEE International Symposium on Semiconductor Manufacturing conference proceedings · 2018
Yusuke Suzuki, Shinya Iwashita, Toshiki Sato, Hitoshi Yonemichi, Hironori Moki, Tsuyoshi Moriya
Read original paper ↗ ·Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning
The Journal of Physical Chemistry C · 2018
Grace C. Anderson, Benjamin Schweitzer, Ryther Anderson, Diego A. Gómez‐Gualdrón
Read original paper ↗ ·Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures
Modelling and Simulation in Materials Science and Engineering · 2018
Anurag Jha, Anand R. Chandrasekaran, Chiho Kim, Rampi Ramprasad
Read original paper ↗ ·Statistical Learning of Discrete States in Time Series
The Journal of Physical Chemistry B · 2018
Hao Li, Haw Yang
Read original paper ↗ ·Formatting biological big data for modern machine learning in drug discovery
Wiley Interdisciplinary Reviews Computational Molecular Science · 2018
Miquel Duran‐Frigola, Adrià Fernández‐Torras, Martino Bertoni, Patrick Aloy
Read original paper ↗ ·A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space
Chemical Science · 2019
Jan Halborg Jensen
Read original paper ↗ ·A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Chemical Science · 2019
Jon Paul Janet, Chenru Duan, Tzuhsiung Yang, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Artificial Intelligence and Personalized Medicine
Cancer treatment and research · 2019
Nicholas J. Schork
Read original paper ↗ ·Classification of MAOX phases and semiconductor screening for next-generation energy conversion ceramic materials
Journal of Materials Chemistry C · 2019
Zhenyu Wang, Yonghong Cheng, Chunming Niu
Read original paper ↗ ·Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches
ACS symposium series · 2019
Philippe Schwaller, Teodoro Laino
Read original paper ↗ ·Designing promising molecules for organic solar cells via machine learning assisted virtual screening
Journal of Materials Chemistry A · 2019
Harikrishna Sahu, Feng Yang, Xiaobo Ye, Jing Ma, Wei‐Hai Fang, Haibo Ma
Read original paper ↗ ·Engineering Dynamic Behavior into Nucleic Acids Guided by Single Molecule Fluorescence Microscopy
Deep Blue (University of Michigan) · 2019
Jieming Li
Read original paper ↗ ·Extending the Small Molecule Similarity Principle to All Levels of Biology
SSRN Electronic Journal · 2019
Miquel Duran‐Frigola, Eduardo Pauls, Oriol Guitart-Pla, Martino Bertoni, Modesto Orozco-Ruiz, Víctor Alcalde, Víctor M. Diaz, Antonio Berenguer, David Amat, Teresa Juan-Blanco, Antonio Garcı́a de Herreros, Patrick Aloy
Read original paper ↗ ·How to Formulate Biobased Surfactants Through the HLD-NAC Model
Elsevier eBooks · 2019
Edgar Acosta, Suryavarshini Sundar
Read original paper ↗ ·Machine learning enables long time scale molecular photodynamics simulations
Chemical Science · 2019
Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger, Sebastian Mai, Leticia González, Philipp Marquetand
Read original paper ↗ ·Machine learning for renewable energy materials
Journal of Materials Chemistry A · 2019
Geun Ho Gu, Juhwan Noh, I. P. Kim, Yousung Jung
Read original paper ↗ ·Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies
Venue not supplied by source · 2019
Mojtaba Haghighatlari
Read original paper ↗ ·Mapping binary copolymer property space with neural networks
Chemical Science · 2019
Liam Wilbraham, Reiner Sebastian Sprick, Kim E. Jelfs, Martijn A. Zwijnenburg
Read original paper ↗ ·Nanoinformatics, and the big challenges for the science of small things
Nanoscale · 2019
Amanda Susan Barnard, Benyamin Motevalli, Amanda J. Parker, Julia Melisande Theresa Agatha Fischer, Christopher A. Feigl, George Opletal
Read original paper ↗ ·Progress and prospects for accelerating materials science with automated and autonomous workflows
Chemical Science · 2019
Helge Sören Stein, John M. Gregoire
Read original paper ↗ ·The inverse design of structural color using machine learning
Nanoscale · 2019
Zhao Huang, Xin Liu, Jianfeng Zang
Read original paper ↗ ·Inverse Problems
Foundations of Science · 2019
Mario Bunge
Read original paper ↗ ·Implementation of Statistical Learning Methods to Develop Guidelines for the Design of PLA-Based Composites with High Tensile Strength Values
Industrial & Engineering Chemistry Research · 2019
Burak Alakent, Sezen Soyer‐Uzun
Read original paper ↗ ·Solving the electronic structure problem with machine learning
npj Computational Materials · 2019
Anand R. Chandrasekaran, Deepak Kamal, Rohit Batra, Chiho Kim, Lihua Chen, Rampi Ramprasad
Read original paper ↗ ·From DFT to machine learning: recent approaches to materials science–a review
Journal of Physics Materials · 2019
Gabriel Ravanhani Schleder, A. C. M. Padilha, Carlos Mera Acosta, Marcio Costa, A. Fazzio
Read original paper ↗ ·Nanomaterials Discovery and Design through Machine Learning
Small Methods · 2019
Ming Wang, Ting Wang, Pingqiang Cai, Xiaodong Chen
Read original paper ↗ ·Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators
Journal of Materials Science · 2019
Ghanshyam Pilania, Xiang-Yang Liu, Zhehui Wang
Read original paper ↗ ·110th Anniversary: Unleashing the Full Potential of Quinones for High Performance Aqueous Organic Flow Battery
Industrial & Engineering Chemistry Research · 2019
Pan Sun, Yahua Liu, Yuanyuan Li, Muhammad Aamir Shehzad, Yazhi Liu, Peipei Zuo, Qianru Chen, Zhengjin Yang, Tongwen Xu
Read original paper ↗ ·Advances of machine learning in molecular modeling and simulation
Current Opinion in Chemical Engineering · 2019
Mojtaba Haghighatlari, Johannes Hachmann
Read original paper ↗ ·Recent advances in machine learning towards multiscale soft materials design
Current Opinion in Chemical Engineering · 2019
Nicholas E. Jackson, Michael A. Webb, Juan Pablo
Read original paper ↗ ·Relational Pooling for Graph Representations
arXiv (Cornell University) · 2019
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
Read original paper ↗ ·Training Neural Nets To Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality
The Journal of Physical Chemistry A · 2019
Silvia Amabilino, Lars Andersen Bratholm, Simon J. Bennie, Alain C. Vaucher, Markus Reiher, David R. Glowacki
Read original paper ↗ ·Autonomous Molecular Design: Then and Now
ACS Applied Materials & Interfaces · 2019
Tanja Dimitrov, Christoph Kreisbeck, Jill S. Becker, Alán Aspuru‐Guzik, Semion K. Saikin
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