| Ziyang Zhang and Yingtao Luo. Deep spatial learning with molecular vibration |
ML4Molecules_2020_paper_1.pdf |
| Navneet Bung, Sowmya Ramaswamy Krishnan and Arijit Roy. De novo design of small molecules against drug targets of central nervous system using multi-property optimization |
ML4Molecules_2020_paper_2.pdf |
| Xiaoke Shen, Yang Liu, You Wu and Lei Xie. MoLGNN: Self-Supervised Motif Learning Graph Neural Network for Drug Discovery |
ML4Molecules_2020_paper_4.pdf |
| Hehuan Ma, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye and Junzhou Huang. Multi-View Graph Neural Networks for Molecular Property Prediction |
ML4Molecules_2020_paper_5.pdf |
| Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt and Frank Noé. Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties |
ML4Molecules_2020_paper_6.pdf |
| Shivshankar. Adversarial Stein Training for Graph Energy Models |
ML4Molecules_2020_paper_7.pdf |
| Ke Yu, Shyam Visweswaran and Kayhan Batmanghelich. Hyperbolic Molecular Representation Learning for Drug Repositioning |
ML4Molecules_2020_paper_8.pdf |
| Chloe Ching-Yun Hsu and Samuel Gleason. Identifying Nanoparticle Geometry from Emissivity |
ML4Molecules_2020_paper_9.pdf |
| Oleksandr Gurbych, Maksym Druchok, Dzvenymyra Yarish and Sofiya Garkot. High throughput screening with machine learning |
ML4Molecules_2020_paper_10.pdf |
| Henry Moss and Ryan-Rhys Griffiths. Gaussian Process Molecular Property Prediction with FlowMO |
ML4Molecules_2020_paper_11.pdf |
| Ruby Sedgwick, John Goertz, Ruth Misener, Molly Stevens and Mark van der Wilk. Design of Experiments for Verifying Biomolecular Networks |
ML4Molecules_2020_paper_12.pdf |
| Omar Mahmood, Elman Mansimov, Richard Bonneau and Kyunghyun Cho. Masked Graph Modeling for Molecule Generation |
ML4Molecules_2020_paper_13.pdf |
| Hongyu Shen, Layne Price, Taha Bahadori and Franziska Seeger. Improving Generalizability of Protein Sequence Models With Data Augmentations |
ML4Molecules_2020_paper_14.pdf |
| Shengchao Liu, Andreea Deac, Zhaocheng Zhu and Jian Tang. Structured Multi-View Representations for Drug Combinations |
ML4Molecules_2020_paper_15.pdf |
| Shuo Zhang, Yang Liu and Lei Xie. Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures |
ML4Molecules_2020_paper_16.pdf |
| Gabriel Reder, Jaan Altosaar, Jakub Rajniak, Noemie Elhadad, Susan Holmes and Michael Fischbach. Supervised Topic Modeling for Predicting Chemical Substructure from Mass Spectrometry |
ML4Molecules_2020_paper_17.pdf |
| Yangzesheng Sun, Tyler R. Josephson and J. Ilja Siepmann. Interpretable Learning of Complex Multicomponent Adsorption Equilibria from Self-attention |
ML4Molecules_2020_paper_18.pdf |
| Alain C. Vaucher, Philippe Schwaller and Teodoro Laino. Completion of partial reaction equations |
ML4Molecules_2020_paper_19.pdf |
| Morgan Thomas, Rob Smith, Noel O’Boyle, Chris De Graaf and Andreas Bender. Towards Integrating Structure-Based Design with Deep Generative Models |
ML4Molecules_2020_paper_20.pdf |
| Philippe Schwaller, Alain Vaucher, Teodoro Laino and Jean-Louis Reymond. Data augmentation strategies to improve reaction yield predictions and estimate uncertainty |
ML4Molecules_2020_paper_21.pdf |
| Robin Winter, Frank Noé and Djork-Arné Clevert. Auto-Encoding Molecular Conformations |
ML4Molecules_2020_paper_22.pdf |
| Zelda Mariet, Ghassen Jerfel, Zi Wang, Christof Angermuller, David Belanger, Suhani Vora, Maxwell Bileschi, Lucy Colwell, D Sculley, Dustin Tran and Jasper Snoek. Deep Uncertainty and the Search for Proteins |
ML4Molecules_2020_paper_23.pdf |
| Gustav Šourek, Filip Zelezny and Ondřej Kuželka. Learning with Molecules beyond Graph Neural Networks |
ML4Molecules_2020_paper_24.pdf |
| Nathan Frey and Bharath Ramsundar. Flow-Based Models for Active Molecular Graph Generation |
ML4Molecules_2020_paper_25.pdf |
| Marcin Skwark, Nicolas Lopez Carranza, Thomas Pierrot, Joe Phillips, Slim Said, Alexandre Laterre, Amine Kerkeni, Ugur Sahin and Karim Beguir. Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning |
ML4Molecules_2020_paper_26.pdf |
| Prateeth Nayak, Andrew Silberfarb, Ran Chen, Tulay Muezzinoglu and John Byrnes. Transformer based Molecule Encoding for Property Prediction |
ML4Molecules_2020_paper_27.pdf |
| Justin Fu and Sergey Levine. Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation |
ML4Molecules_2020_paper_28.pdf |
| Dylan Sam and Brenda M. Rubenstein. Hierarchical Clustering Analysis of Spectral Fingerprints for Cheminformatics |
ML4Molecules_2020_paper_29.pdf |
| Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes and Bo Dai. Energy-based View of Retrosynthesis |
ML4Molecules_2020_paper_30.pdf |
| Ava Soleimany, Alexander Amini, Samuel Goldman, Daniela Rus, Sangeeta Bhatia and Connor Coley. Evidential Deep Learning for Guided Molecular Property Prediction and Discovery |
ML4Molecules_2020_paper_31.pdf |
| Wujie Wang, Simon Axelrod and Rafael Gomez-Bombarelli. Differentiable Molecular Simulations for Learning and Control |
ML4Molecules_2020_paper_32.pdf |
| Gregor Simm, Robert Pinsler, Gábor Csányi and José Miguel Hernández Lobato. 3D Molecular Design with Covariant Neural Networks |
ML4Molecules_2020_paper_34.pdf |
| Johannes Gasteiger, Shankari Giri, Johannes T. Margraf and Stephan Günnemann. Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules |
ML4Molecules_2020_paper_35.pdf |
| Lagnajit Pattanaik, Octavian Ganea, Ian Coley, Klavs Jensen, William Green and Connor Coley. Message Passing Networks for Molecules with Tetrahedral Chirality |
ML4Molecules_2020_paper_36.pdf |
| Fergus Imrie, Anthony R. Bradley and Charlotte M. Deane. Constrained Deep Generative Linker Design Using 3D Structural Priors |
ML4Molecules_2020_paper_37.pdf |
| Tuan Le, Marco Bertolini, Marc Arne Boef, Floriane Montanari and Djork-Arné Clevert. Going full hyper: hyperbolic and hypercomplex graph embeddings for ADMET modeling |
ML4Molecules_2020_paper_38.pdf |
| Aneesh Pappu and Brooks Paige. Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning |
ML4Molecules_2020_paper_39.pdf |
| Gianluca Tirimbo, Onur Caylak and Bjorn Baumeier. A Kernel based Machine Learning Approach to Computing Quasiparticle Energies within Many-Body Green’s Functions Theory |
ML4Molecules_2020_paper_40.pdf |
| Bo Pang, Tian Han and Ying Nian Wu. Learning Latent Space Energy-Based Prior Model for Molecule Generation |
ML4Molecules_2020_paper_41.pdf |
| Hankook Lee, Sungsoo Ahn, Seung-Woo Seo, You Young Song, Eunho Yang, Sung Ju Hwang and Jinwoo Shin. RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning |
ML4Molecules_2020_paper_42.pdf |
| Stephan Eismann, Patricia Suriana, Bowen Jing, Raphael Townshend and Ron Dror. Protein model quality assessment using rotation-equivariant, hierarchical neural networks |
ML4Molecules_2020_paper_43.pdf |
| Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael Townshend and Ron Dror. Learning from Protein Structure with Geometric Vector Perceptrons |
ML4Molecules_2020_paper_44.pdf |
| Davide Bacciu and Danilo Numeroso. Explaining Deep Graph Networks with Molecular Counterfactuals |
ML4Molecules_2020_paper_45.pdf |
| Vitali Nesterov, Mario Wieser and Volker Roth. 3DMolNet: A Generative Network for Molecular Structures |
ML4Molecules_2020_paper_46.pdf |
| Peter Bjørn Jørgensen and Arghya Bhowmik. DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction |
ML4Molecules_2020_paper_47.pdf |
| Nina Lukashina, Alisa Alenicheva, Elizaveta Vlasova, Artem Kondiukov, Aigul Khakimova, Emil Magerramov, Nikita Churikov and Aleksei Shpilman. Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation |
ML4Molecules_2020_paper_48.pdf |
| Hao Zhu, Liping Liu and Soha Hassoun. Using Graph Neural Networks for Mass Spectrum Prediction |
ML4Molecules_2020_paper_50.pdf |
| Gowoon Cheon, Lusann Yang, Kevin McCloskey, Evan Reed and Ekin Cubuk. Crystal Structure Search with Random Relaxations Using Graph Networks |
ML4Molecules_2020_paper_51.pdf |
| Amina Mollaysa, Brooks Paige and Alexandros Kalousis. Conditional generation of molecules from disentangled representations |
ML4Molecules_2020_paper_52.pdf |
| Gian Marco Visani, Michael Hughes and Soha Hassoun. Enzyme Promiscuity Prediction using Hierarchical Multi-Label Classification |
ML4Molecules_2020_paper_53.pdf |
| Agnieszka Pocha, Tomasz Danel and Lukasz Maziarka. Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction |
ML4Molecules_2020_paper_54.pdf |
| Ryan Henderson, Djork-Arné Clevert and Floriane Montanari. Gini in a Bottleneck: Sparse Molecular Representations for Graph Convolutional Neural Networks |
ML4Molecules_2020_paper_55.pdf |
| Stephen Ra, Vishnu Sresht and Farhan Damani. Black box recursive translations for molecular optimization |
ML4Molecules_2020_paper_56.pdf |
| Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J. Bygrave, Daniel G. A. Smith, Animashree Anandkumar, Frederick R. Manby and Thomas F. Miller III. Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces |
ML4Molecules_2020_paper_57.pdf |
| Kobi Felton, Daniel Wigh and Alexei Lapkin. Multi-task Bayesian Optimization of Chemical Reactions |
ML4Molecules_2020_paper_58.pdf |
| Tian Xie, Arthur France-Lanord, Yanming Wang, Jeffrey Lopez, Michael Austin Stolberg, Megan Hill, Graham Michael Leverick, Rafael Gomez-Bombarelli, Jeremiah A. Johnson, Yang Shao-Horn and Jeffrey Grossman. Accelerate the screening of complex materials by learning to reduce random and systematic errors |
ML4Molecules_2020_paper_59.pdf |
| Wengong Jin, Regina Barzilay and Tommi Jaakkola. Predicting Synergistic Drug Combinations for COVID with Biological Bottleneck Models |
ML4Molecules_2020_paper_60.pdf |
| Magdalena Wiercioch and Johannes Kirchmair. Deep Neural Network Approach to Predict Properties of Drugs and Drug-Like Molecules |
ML4Molecules_2020_paper_61.pdf |
| Luca Thiede, Mario Krenn, Alán Aspuru-Guzik and Akshat Nigam. Curiosity in exploring chemical space: Intrinsic rewards for molecular reinforcement learning |
ML4Molecules_2020_paper_62.pdf |
| Alexander Tong, Frederik Wenkel, Kincaid MacDonald, Smita Krishnaswamy and Guy Wolf. Data-driven Learning of Geometric Scattering Networks |
ML4Molecules_2020_paper_63.pdf |
| George Lamb and Brooks Paige. Bayesian GNNs for Molecular Property Prediction |
ML4Molecules_2020_paper_64.pdf |
| Walid Ahmad. Evaluating chemical descriptors with the loss-data framework |
ML4Molecules_2020_paper_65.pdf |
| Cynthia Shen, Mario Krenn, Sagi Eppel and Alan Aspuru-Guzik. Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design with surjective representations |
ML4Molecules_2020_paper_66.pdf |
| Seyone Chithrananda, Gabriel Grand and Bharath Ramsundar. ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction |
ML4Molecules_2020_paper_67.pdf |
| Shehtab Zaman, Christopher Owen, Kenneth Chiu and Michael Lawler. Graph Neural Network for Metal Organic Framework Potential Energy Approximation |
ML4Molecules_2020_paper_68.pdf |
| Ali Raza, Faaiq Waqar, Arni Sturluson, Cory Simon and Xiaoli Fern. Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks |
ML4Molecules_2020_paper_69.pdf |
| Miguel Garcia-Ortegon, Andreas Bender, Carl Rasmussen, Hiroshi Kajino and Sergio Bacallado. Combining variational autoencoder representations with structural descriptors improves prediction of docking scores |
ML4Molecules_2020_paper_70.pdf |
| Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski, Paweł Włodarczyk-Pruszyński, Yoshua Bengio and Marwin Segler. RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design |
ML4Molecules_2020_paper_72.pdf |
| Tomasz Danel, Maciej Szymczak, Łukasz Maziarka, Igor Podolak, Jacek Tabor and Stanisław Jastrzębski. De Novo Drug Design with a Docking Score Proxy |
ML4Molecules_2020_paper_73.pdf |
| Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato and Mohamed Ahmed. Molecular representation learning with language models and domain-relevant auxiliary tasks |
ML4Molecules_2020_paper_74.pdf |
| Kangway Chuang and Michael Keiser. Attention-Based Learning on Molecular Ensembles |
ML4Molecules_2020_paper_75.pdf |