Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction | Paper Citation Record

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Unresolved cited work

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Very Deep Convolutional Networks for Large-Scale Image Recognition

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Zeiler and Rob Fergus

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Going deeper with convolutions

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Deep residual learn- ing for image recognition

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This paper cites A comprehensive comparison of molecular feature representations for use in predictive modeling.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction A comprehensive comparison of molecular feature representations for use in predictive modeling

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This paper cites Large-scale comparison of machine learning methods for drug target prediction on ChEMBL.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

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This paper cites Could 163 graph neural networks learn better molecular representation for drug discov- ery? A comparison study of descriptor-based and graph-based models.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Could 163 graph neural networks learn better molecular representation for drug discov- ery? A comparison study of descriptor-based and graph-based models

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Molecular Contrastive Learning of Representations via Graph Neural Networks

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This paper cites Using domain-specific fingerprints generated through neural networks to enhance ligand-based virtual screening.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Using domain-specific fingerprints generated through neural networks to enhance ligand-based virtual screening

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This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

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This paper cites Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?

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This paper cites Learn- ing continuous and data-driven molecular descriptors by translating equivalent chemical representations.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Learn- ing continuous and data-driven molecular descriptors by translating equivalent chemical representations

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This paper cites Handbook of Molecular Descriptors.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Handbook of Molecular Descriptors

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This paper cites Fingerprints, and other molec- ular descriptions for database analysis and searching.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Fingerprints, and other molec- ular descriptions for database analysis and searching

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Extended-connectivity fingerprints

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Schoenholz, Patrick F

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Semi-Supervised Classification with Graph Convolutional Networks

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This paper cites Molecular graph convolutions: Moving beyond fingerprints.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Molecular graph convolutions: Moving beyond fingerprints

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This paper cites Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Al´ an Aspuru-Guzik, and Ryan P.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Al´ an Aspuru-Guzik, and Ryan P

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Strategies for Pre-training Graph Neural Networks

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This paper cites Analyzing learned molecular representations for property prediction.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Analyzing learned molecular representations for property prediction

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Yu Philip

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This paper cites A compact review of molecu- lar property prediction with graph neural networks.

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction A compact review of molecu- lar property prediction with graph neural networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Gated Graph Sequence Neural Networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Interaction networks for learning about objects, relations and physics

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Convolutional neural networks on graphs with fast localized spectral filtering

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Chemi-Net: A molecular graph convo- lutional network for accurate drug property prediction

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction How Powerful are Graph Neural Networks?

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Towards deeper graph neural networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Universal readout for graph convolutional neural networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Quantitative evaluation of explainable graph neural networks for molecular property predic- tion

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Geometric deep learning au- tonomously learns chemical features that outperform those engineered by do- main experts

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Edge Attention-based Multi-Relational Graph Convolutional Networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Learning Graph-Level Representation for Drug Discovery

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Exposing the limitations of molecular machine learning with activity cliffs

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction QSAR, rational approaches to the design of bioactive compounds

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Maggiora

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Sheridan, Prabha Karnachi, Matthew Tudor, Yuting Xu, Andy Liaw, Falgun Shah, Alan C

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Medina-Franco, Yunierkis P´ erez-Castillo, Orazio Nicolotti, M

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Recent progress in understanding activity cliffs and their utility in medicinal chem- istry: miniperspective

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Evolving concept of ac- tivity cliffs

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Advances in exploring activity cliffs

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Mor- ris

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction (2022) Reduced collision fingerprints and pairwise molec- ular comparisons for explainable property prediction using deep learning

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Prediction of molecular properties using molecular topo- graphic map

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Prediction of activity cliffs on the basis of images using convolutional neural networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Can one hear the shape of a molecule (from its Coulomb matrix eigenvalues)? Journal of Chemical Information and Modeling, 60(8):3804–3811, 2020

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Uni-Mol: A universal 3D molecu- lar representation learning framework

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Keith Lloyd, and Robin J

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction SMILES, a chemical language and information system

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Weininger

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Graphical depiction of chemical structures

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Image: Deriving the SMILES represen- tation of a chemical molecule, Shown example: ciprofloxacin, a fluoroquinolone antibiotic

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction InChI - the worldwide chemical structure identifier standard

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Wei, David Duvenaud, Jos´ e Miguel Hern´ andez-Lobato, Benjam ´ ın S´ anchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction DeepSMILES: An adaptation of SMILES for use in machine-learning of chemical structures

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Mold 2, molecular descriptors 169 from 2D structures for chemoinformatics and toxicoinformatics

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Molecular descriptors

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction RDKit: Open-source cheminformatics

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Molecular representation learning with language models and domain-relevant auxiliary tasks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Durant, Burton A

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction URL https:// www.daylight.com/dayhtml/doc/theory/theory.finger.html

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Open-source platform to benchmark fin- gerprints for ligand-based virtual screening

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Webel, Talia B

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Benchmarking Graph Neural Networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Continuous Representation of Molecules Using Graph Variational Autoencoder

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Open graph benchmark: Datasets for machine learning on graphs

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Multilayer feedforward networks are universal approximators

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Understanding graph isomorphism net- work for rs-fMRI functional connectivity analysis

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction The reduction of a graph to canonical form and the algebra which appears therein

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Zafeiriou, and Michael Bron- stein

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Evaluating Deep Graph Neural Networks

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veliˇ ckovi´ c, James Kirkpatrick, and 172 Peter Battaglia

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Measuring and relieving the over-smoothing problem for graph neural networks from the topo- logical view

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Fast Graph Representation Learning with PyTorch Geometric

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Data set modelability by QSAR

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Leadley et al

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction From activity cliffs to activity ridges: Informative data structures for SAR analysis

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Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction Activity cliff clusters as a source of structure–activity relationship information

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