Papers citing Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
QSAR: What Else?
Methods in molecular biology · 2018-01-01
Giuseppina C. Gini
Read original paper ↗ ·Toward the Systematic Design of Complex Materials from Structural Motifs
ProQuest Demo Repository · 2018-01-01
Tess Smidt
Read original paper ↗ ·Towards operando computational modeling in heterogeneous catalysis
Chemical Society Reviews · 2018-01-01
Lukáš Grajciar, Christopher James Heard, A. A. Bondarenko, Mikhail V. Polynski, Jittima Meeprasert, Evgeny A. Pidko, Petr Nachtigall
Read original paper ↗ ·PHOENICS: A universal deep Bayesian optimizer
arXiv (Cornell University) · 2018-01-04
Florian Häse, Loı̈c M. Roch, Christoph Kreisbeck, Alán Aspuru‐Guzik
Read original paper ↗ ·Artificial intelligence in drug combination therapy
Briefings in Bioinformatics · 2018-01-05
Igor F. Tsigelny
Read original paper ↗ ·Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
arXiv (Cornell University) · 2018-01-18
Yibo Li, Liangren Zhang, Zhenming Liu
Read original paper ↗ ·The rise of deep learning in drug discovery
Drug Discovery Today · 2018-01-31
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, Thomas Blaschke
Read original paper ↗ ·The Matter Simulation (R)evolution
ACS Central Science · 2018-02-06
Alán Aspuru‐Guzik, Roland H. Lindh, Markus Reiher
Read original paper ↗ ·Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
The Journal of Physical Chemistry Letters · 2018-02-09
Jon Paul Janet, Lydia Chan, Heather J. Kulik
Read original paper ↗ ·Junction Tree Variational Autoencoder for Molecular Graph Generation
arXiv (Cornell University) · 2018-02-12
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Read original paper ↗ ·Leveraging the Exact Likelihood of Deep Latent Variable Models
IT University Of Copenhagen (IT University of Copenhagen) · 2018-02-13
Pierre‐Alexandre Mattei, Jes Frellsen
Read original paper ↗ ·Designing Random Graph Models Using Variational Autoencoders With Applications to Chemical Design.
arXiv (Cornell University) · 2018-02-14
Bidisha Samanta, Abir De, Niloy Ganguly, Manuel Gomez-Rodriguez
Read original paper ↗ ·Edge Attention-based Multi-Relational Graph Convolutional Networks
arXiv (Cornell University) · 2018-02-14
Shang, Chao, Qinqing Liu, Ko‐Shin Chen, Jiangwen Sun, Jin Lü, Jinfeng Yi, Jinbo Bi
Read original paper ↗ ·Sparse QSAR modelling methods for therapeutic and regenerative medicine
Journal of Computer-Aided Molecular Design · 2018-02-14
David Alan Winkler
Read original paper ↗ ·Simulations meet machine learning in structural biology
Current Opinion in Structural Biology · 2018-02-21
Adrià Pérez, Gerard Martínez-Rosell, Gianni De Fabritiis
Read original paper ↗ ·Syntax-Directed Variational Autoencoder for Structured Data
arXiv (Cornell University) · 2018-02-24
Hanjun Dai, Tian, Yingtao, Bo Dai, Steven Skiena, Le Song
Read original paper ↗ ·How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
IEEE Winter Conference on Applications of Computer Vision · 2018-03-01
Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan Baker
Read original paper ↗ ·Learning Deep Generative Models of Graphs
arXiv (Cornell University) · 2018-03-08
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia
Read original paper ↗ ·Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
The Journal of Chemical Physics · 2018-03-15
Christoph Wehmeyer, Frank Noé
Read original paper ↗ ·Convolutional neural networks for atomistic systems
Computational Materials Science · 2018-03-16
Kevin Ryczko, Kyle Mills, Iryna Luchak, Christa M. Homenick, Isaac Tamblyn
Read original paper ↗ ·Structure prediction of boron-doped graphene by machine learning
The Journal of Chemical Physics · 2018-03-19
Sae Dieb, Zhufeng Hou, Koji Tsuda
Read original paper ↗ ·Adversarial Threshold Neural Computer for Molecular de Novo Design
Molecular Pharmaceutics · 2018-03-23
Eugene Lane, Arip Asadulaev, Quentin Vanhaelen, Yan A. Ivanenkov, Anastasia V. Aladinskaya, Alex Aliper, Alex A. Zhavoronkov
Read original paper ↗ ·Fréchet ChemblNet Distance: A metric for generative models for molecules.
arXiv (Cornell University) · 2018-03-26
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, Günter Klambauer
Read original paper ↗ ·Constant size descriptors for accurate machine learning models of molecular properties
The Journal of Chemical Physics · 2018-03-27
Christopher R. Collins, Geoffrey J. Gordon, O. Anatole von Lilienfeld, David Yaron
Read original paper ↗ ·Can AI reproduce observed chemical diversity?
bioRxiv (Cold Spring Harbor Laboratory) · 2018-03-30
Mostapha Benhenda
Read original paper ↗ ·Machine learning in catalysis
Nature Catalysis · 2018-04-10
John R. Kitchin
Read original paper ↗ ·Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database
ACS Omega · 2018-04-11
Hongwei Wang, Fang Liu, Tiange Dong, Likai Du, Dongju Zhang, Jun Gao
Read original paper ↗ ·Deep learning improves prediction of drug–drug and drug–food interactions
Proceedings of the National Academy of Sciences · 2018-04-16
Jae Yong Ryu, Hyun Uk Kim, Sang Yup Lee
Read original paper ↗ ·Designing Anticancer Peptides by Constructive Machine Learning
ChemMedChem · 2018-04-21
Francesca Grisoni, Claudia S. Neuhaus, Gisela Gabernet, Alex T. Müller, Jan A. Hiss, Gisbert Schneider
Read original paper ↗ ·Accelerating the discovery of materials for clean energy in the era of smart automation
Nature Reviews Materials · 2018-04-26
Daniel P. Tabor, Loı̈c M. Roch, Semion K. Saikin, Christoph Kreisbeck, Dennis Sheberla, Joseph H. Montoya, Shyam Dwaraknath, Muratahan Aykol, C. Ortiz, Hermann Tribukait, Carlos Amador‐Bedolla, Christoph Josef Brabec, Benji Maruyama, Kristin Aslaug Persson, Alán Aspuru‐Guzik
Read original paper ↗ ·Machine Learning in Computer-Aided Synthesis Planning
Accounts of Chemical Research · 2018-05-01
Connor W. Coley, William H. Green, Klavs F. Jensen
Read original paper ↗ ·Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
Bioinformatics · 2018-05-04
Marta M. Stepniewska-Dziubinska, Piotr Zielenkiewicz, Pawel S. Siedlecki
Read original paper ↗ ·Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category
Frontiers in Chemistry · 2018-05-09
Abraham Yosipof, Rita Cardoso Guedes, Alfonso T. García‐Sosa
Read original paper ↗ ·Cheminformatics in Drug Discovery, an Industrial Perspective
Molecular Informatics · 2018-05-18
Hongming Chen, Thierry Kogej, Ola Engkvist
Read original paper ↗ ·Constrained Graph Variational Autoencoders for Molecule Design
arXiv (Cornell University) · 2018-05-23
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
Read original paper ↗ ·Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
arXiv (Cornell University) · 2018-05-28
Seongok Ryu, Jaechang Lim, Seung Hwan Hong, Woo Youn Kim
Read original paper ↗ ·Deeply learning molecular structure-property relationships using graph attention neural network.
arXiv (Cornell University) · 2018-05-28
Seongok Ryu, Jaechang Lim, Woo Youn Kim
Read original paper ↗ ·Geometric Deep Learning Autonomously Learns Chemical Features That Outperform Those Engineered by Domain Experts
Molecular Pharmaceutics · 2018-06-04
Patrick Hop, Brandon Allgood, Jessen Yu
Read original paper ↗ ·Machine learning-based screening of complex molecules for polymer solar cells
The Journal of Chemical Physics · 2018-06-06
Peter Bjørn Jørgensen, Murat Mesta, Suranjan Shil, Juan Maria García Lastra, Karsten W. Jacobsen, Kristian Sommer Thygesen, Mikkel Nørgaard Schmidt
Read original paper ↗ ·Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
arXiv (Cornell University) · 2018-06-07
Jiaxuan You, Bowen Liu, Rex Ying, Vijay S. Pande, Jure Leskovec
Read original paper ↗ ·Globally Optimal Catalytic Fields – Inverse Design of Abstract Embeddings for Maximum Reaction Rate Acceleration
Journal of Chemical Theory and Computation · 2018-06-08
Mark Dittner, Bernd Hartke
Read original paper ↗ ·N-Gram Graph, A Novel Molecule Representation.
arXiv (Cornell University) · 2018-06-24
Shengchao Liu, Thevaa Chandereng, Yingyu Liang
Read original paper ↗ ·A Scalable, Multiplexed Assay for Decoding Receptor-Ligand Interactions
bioRxiv (Cold Spring Harbor Laboratory) · 2018-06-28
Eric M. Jones, Rishi Jajoo, Daniel Cancilla, Nathan B. Lubock, Jeff Wang, Megan Satyadi, Rocky Cheung, Claire A. de March, Hiroaki Matsunami, Sriram Kosuri
Read original paper ↗ ·Controlling an organic synthesis robot with machine learning to search for new reactivity
Nature · 2018-07-01
Jarosław M. Granda, Liva Donina, Vincenza Dragone, De‐Liang Long, Leroy Cronin
Read original paper ↗ ·Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors
Advanced Energy Materials · 2018-07-02
Harikrishna Sahu, Weining Rao, Alessandro Troisi, Haibo Ma
Read original paper ↗ ·Structured Variationally Auto-encoded Optimization.
International Conference on Machine Learning · 2018-07-03
Xiaoyu Lu, Javier González, Zhenwen Dai, Neil David Lawrence
Read original paper ↗ ·Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning
ChemRxiv · 2018-07-11
Sherif Abdulkader Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph George Shapter, David Alan Winkler, Michael J. Ford
Read original paper ↗ ·DeepDTA: deep drug–target binding affinity prediction
Bioinformatics · 2018-07-14
Hakime Öztürk, Arzucan Özgür, Elif Özkırımlı
Read original paper ↗ ·Conditional Molecular Design with Deep Generative Models
Journal of Chemical Information and Modeling · 2018-07-17
Seokho Kang, Kyunghyun Cho
Read original paper ↗ ·Artificial intelligence in drug design
Science China Life Sciences · 2018-07-18
Feisheng Zhong, Jing Xing, Xutong Li, Xiaohong Liu, Zunyun Fu, Zhaoping Xiong, Dong Lu, Xiaolong Wu, Jihui Zhao, Xiaoqin Tan, Fei Li, Xiaomin Luo, Zhaojun Steven Li, Kaixian Chen, Mingyue Zheng, Hualiang Jiang
Read original paper ↗ ·