Papers citing Deep learning for molecular design—a review of the state of the art
Hard-threshold neural network-based prediction of organic synthetic outcomes
BMC Chemical Engineering · 2020
Haoyang Hu, Zhihong Yuan
Read original paper ↗ ·Augmenting Molecular Images with Vector Representations as a Featurization Technique for Drug Classification
IEEE International Conference on Acoustics Speech and Signal Processing · 2020
Daniel de Marchi, Amarjit S. Budhiraja
Read original paper ↗ ·GEN: highly efficient SMILES explorer using autodidactic generative examination networks
Journal of Cheminformatics · 2020
Ruud van Deursen, Peter Ertl, Igor V. Tetko, Guillaume Godin
Read original paper ↗ ·Continuous Representation of Molecules Using Graph Variational Autoencoder
arXiv (Cornell University) · 2020
Mohammadamin Tavakoli, Pierre Baldi
Read original paper ↗ ·Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery
Annual Review of Materials Research · 2020
Changwon Suh, Clyde Fare, James A. Warren, Edward O. Pyzer‐Knapp
Read original paper ↗ ·CReM: chemically reasonable mutations framework for structure generation
Journal of Cheminformatics · 2020
Павел Геннадьевич Полищук
Read original paper ↗ ·Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
arXiv (Cornell University) · 2020
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Guo Tang, Sarath Chandar, Yoshua Bengio
Read original paper ↗ ·A Review of Deep Learning Methods for Antibodies
Antibodies · 2020
Jordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati, S. Parish, Brenda P. Medellin, Monica Berrondo
Read original paper ↗ ·On Failure Modes of Molecule Generators and Optimizers
ChemRxiv · 2020
Philipp Renz, Dries Van Rompaey, Jörg K. Wegner, Sepp Hochreiter, Günter Klambauer
Read original paper ↗ ·Machine Learning on Graphs: A Model and Comprehensive Taxonomy
arXiv (Cornell University) · 2020
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin P. Murphy
Read original paper ↗ ·High-Throughput Molecular Dynamics Simulations and Validation of Thermophysical Properties of Polymers
ChemRxiv · 2020
Mohammad Atif Faiz Afzal, Andrea Robben Browning, Alexander Goldberg, Mathew David Halls, Jacob L Gavartin, Tsuguo Morisato, Thomas F. Hughes, David J. Giesen, Joseph E. Goose
Read original paper ↗ ·kGCN: a graph-based deep learning framework for chemical structures
Journal of Cheminformatics · 2020
Ryosuke Kojima, Shoichi Ishida, Masateru Ohta, Hiroaki Iwata, Teruki Honma, Yasushi Okuno
Read original paper ↗ ·Machine-Learning Coupled Cluster Properties through a Density Tensor Representation
The Journal of Physical Chemistry A · 2020
Benjamin G. Peyton, Connor G. Briggs, Ruhee D’Cunha, Johannes T. Margraf, T. Daniel Crawford
Read original paper ↗ ·Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks
Nature Machine Intelligence · 2020
Panagiotis-Christos Kotsias, Josep Arús‐Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, Esben Jannik Bjerrum
Read original paper ↗ ·Artificial intelligence in chemistry and drug design
Journal of Computer-Aided Molecular Design · 2020
Nathan Brown, Peter Ertl, Richard A. Lewis, Torsten Luksch, Daniel Reker, Nadine Schneider
Read original paper ↗ ·Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Chemical Reviews · 2020
Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit
Read original paper ↗ ·Machine learning and excited-state molecular dynamics
Machine Learning Science and Technology · 2020
Julia Westermayr, Philipp Marquetand
Read original paper ↗ ·Role of Artificial Intelligence and Machine Learning in Nanosafety
Small · 2020
David Alan Winkler
Read original paper ↗ ·Building powerful and equivariant graph neural networks with message-passing
arXiv (Cornell University) · 2020
Clément Vignac, Andreas Loukas, Pascal Frossard B
Read original paper ↗ ·Building powerful and equivariant graph neural networks with structural message-passing
arXiv (Cornell University) · 2020
Clément Vignac, Andreas Loukas, Pascal Frossard B
Read original paper ↗ ·Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
npj Computational Materials · 2020
Yabo Dan, Yong Zhao, Xiang Li, Shaobo Li, Ming Hu, Jianjun Hu
Read original paper ↗ ·Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions
Computers & Chemical Engineering · 2020
Abdulelah S. Alshehri, Rafiqul Gani A, Fengqi You
Read original paper ↗ ·The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry
ChemRxiv · 2020
Aditya Raymond Thawani, Ryan‐Rhys Griffiths, Arian R. Jamasb, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander A. Aldrick, Alpha A. Lee
Read original paper ↗ ·ProteiNN: Intrinsic-Extrinsic Convolution and Pooling for Scalable Deep Protein Analysis
arXiv (Cornell University) · 2020
Pedro Hermosilla, Marco Schäfer, Matěj Lang, Gloria Fackelmann, Pere‐Pau Vázquez, Barbora Kozlíková, Michael Krone, Tobias Ritschel, Timo Ropinski
Read original paper ↗ ·The Advent of Generative Chemistry
ACS Medicinal Chemistry Letters · 2020
Quentin Vanhaelen, Yen‐Chu Lin, Alex A. Zhavoronkov
Read original paper ↗ ·Machine Learning in Science – A Role for Mechanical Sympathy?
Royal Society of Chemistry eBooks · 2020
Hugh Cartwright
Read original paper ↗ ·Electronic Structure Modeling of Metal–Organic Frameworks
Chemical Reviews · 2020
Jenna L. Mancuso, Austin M. Mroz, Khoa N. Le, Christopher H. Hendon
Read original paper ↗ ·Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design
Molecules · 2020
Eugene Lin, Chieh‐Hsin Lin, Hsien‐Yuan Lane
Read original paper ↗ ·Transfer Learning for Drug Discovery
Journal of Medicinal Chemistry · 2020
Chenjing Cai, Shiwei Wang, Youjun Xu, Weilin Zhang, Ke Tang, Qi Ouyang, Luhua Lai, Jianfeng Pei
Read original paper ↗ ·Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models
ChemRxiv · 2020
Andy S. Anker, Emil T. S. Kjær, Erik B. Dam, Simon J. L. Billinge, Kirsten M. Ø. Jensen, Raghavendra Selvan
Read original paper ↗ ·Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models
ChemRxiv · 2020
Andy S. Anker, Emil T. S. Kjær, Erik B. Dam, Simon J. L. Billinge, Kirsten M. Ø. Jensen, Raghavendra Selvan
Read original paper ↗ ·Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN
Carbon · 2020
Yuan Dong, Dawei Li, Chi Zhang, Chuhan Wu, Hong Wang, Ming Xin, Jianlin Jack Cheng, Jian Lin
Read original paper ↗ ·Generative Model for Proposing Drug Candidates Satisfying Anticancer Properties Using a Conditional Variational Autoencoder
ACS Omega · 2020
Sunghoon Joo, Min Soo Kim, Jaeho Yang, Jeahyun Park
Read original paper ↗ ·Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning
ACS Combinatorial Science · 2020
Alexander T. Taguchi, James Boyd, Chris W. Diehnelt, Joseph Barten Legutki, Zhan‐Gong Zhao, Neal W. Woodbury
Read original paper ↗ ·Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Machine Learning Science and Technology · 2020
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru‐Guzik
Read original paper ↗ ·Chemical space exploration: how genetic algorithms find the needle in the haystack
PeerJ Physical Chemistry · 2020
Emilie S. Henault, Maria Harris Rasmussen, Jan Halborg Jensen
Read original paper ↗ ·Photoswitching Molecular Junctions: Platforms and Electrical Properties
ChemPhysChem · 2020
Youngsang Kim
Read original paper ↗ ·Deep computational pathology in breast cancer
Seminars in Cancer Biology · 2020
Andrea Duggento, Allegra Conti, Alessandro Mauriello, Maria Guerrisi, Nicola Toschi
Read original paper ↗ ·Computational Approaches for De Novo Drug Design: Past, Present, and Future
Methods in molecular biology · 2020
Xuhan Liu, Adriaan P. IJzerman, Gerard J. P. van Westen
Read original paper ↗ ·Molecular Inverse-Design Platform for Material Industries
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · 2020
Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu, Victoria A. Piunova, Dmitry Yu. Zubarev, Daniel P. Sanders, Jed W. Pitera, Makoto Kogoh, Takumi Hongo, Yenwei Cheng, Wolf Bocanett, Hideaki Nakashika, Akihiro Fujita, Tsuchiya Yuta, Katsuhiko Hino, Kentaro Yano, Shuichi Hirose, Hiroki Toda, Yasumitsu Orii, Daiju Nakano
Read original paper ↗ ·Continuous Molecular Representations of Ionic Liquids
The Journal of Physical Chemistry B · 2020
Wesley Beckner, Chowdhury M. Ashraf, James Lee, David A. C. Beck, Jim Pfaendtner
Read original paper ↗ ·Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
arXiv (Cornell University) · 2020
Enrui Zhang, Minglang Yin, George Em Karniadakis
Read original paper ↗ ·Transfer Learning as Tool to Enhance Predictions of Molecular Properties Based on 2D Projections
Advanced Theory and Simulations · 2020
Niklas Julian Lentelink, Stefan Palkovits
Read original paper ↗ ·EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation
Journal of Cheminformatics · 2020
Jules Leguy, Thomas Cauchy, Marta Glavatskikh, Béatrice Duval, Benoit Da Mota
Read original paper ↗ ·Compressed graph representation for scalable molecular graph generation
Journal of Cheminformatics · 2020
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Kyoham Shin, Seokho Kang
Read original paper ↗ ·Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
arXiv (Cornell University) · 2020
Navid Shervani-Tabar, Nicholas J. Zabaras
Read original paper ↗ ·Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
Journal of Computer-Aided Molecular Design · 2020
Tobias Morawietz, Nongnuch Artrith
Read original paper ↗ ·Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics
arXiv (Cornell University) · 2020
Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy, Payel Das
Read original paper ↗ ·MolFinder: An Efficient Global Molecular Property Optimization and Search Algorithm Using SMILES
ChemRxiv · 2020
Yongbeom Kwon, Juyong Lee
Read original paper ↗ ·MolFinder: An Efficient Global Molecular Property Optimization and Search Algorithm Using SMILES
ChemRxiv · 2020
Yongbeom Kwon, Juyong Lee
Read original paper ↗ ·