Connor W. Coley

Computational Theory and Mathematics · 2017–2023

A researcher in the Science corpus: 16 papers; trusts 43 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 274 citations into it; #1,029 of 12,182 by trust here, #246 by citations
• Materials Chemistry — 215 citations into it; #610 of 11,513 by trust here, #2,128 by citations
• Molecular Biology — 212 citations into it; #9,744 of 17,773 by trust here, #4,377 by citations
• Computational Chemistry & Drug Discovery — 165 citations into it; #125 of 425 by trust here, #109 by citations
• Artificial Intelligence — 157 citations into it; #14,012 of 35,345 by trust here, #11,327 by citations
• Graph Learning — 60 citations into it; #635 of 693 by trust here, #355 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Generative Models — 40 citations into it

Whom Connor W. Coley trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Marwin H. S. Segler
2. Wengong Jin
3. Diederik P. Kingma
4. Regina Barzilay
5. Tommi S. Jaakkola
6. Geoffrey E. Hinton
7. Mark P. Waller
8. Yoshua Bengio
9. Tao Lei
10. Michael H. Abraham

Who trusts Connor W. Coley overall

1. Philippe Schwaller
2. José Jiménez-Luna
3. Rohit Batra
4. Wengong Jin
5. Philipp Seidl
6. Francesca Grisoni
7. Igor V. Tetko
8. Chaopeng Shen
9. Tânia F Cova
10. Gisbert Schneider

Papers from Connor W. Coley's perspective

Ordered by how strongly the researchers Connor W. Coley trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• A graph-convolutional neural network model for the prediction of chemical reactivity (2018) — 515 citations
• Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network (2017) — 332 citations
• Prediction of Organic Reaction Outcomes Using Machine Learning (2017) — 654 citations
• Analyzing Learned Molecular Representations for Property Prediction (2019) — 1,807 citations
• Differentiable Scaffolding Tree for Molecular Optimization (2021) — 92 citations
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles (2021) — 176 citations
• A robotic platform for flow synthesis of organic compounds informed by AI planning (2019) — 847 citations
• Machine Learning in Computer-Aided Synthesis Planning. (2018) — 563 citations
• Using Machine Learning To Predict Suitable Conditions for Organic Reactions (2018) — 334 citations
• Computer-Assisted Retrosynthesis Based on Molecular Similarity (2017) — 381 citations
• Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021) — 487 citations
• The Synthesizability of Molecules Proposed by Generative Models (2020) — 344 citations
• Scientific discovery in the age of artificial intelligence (2023) — 1,745 citations
• SCScore: Synthetic Complexity Learned from a Reaction Corpus (2018) — 326 citations
• Uncertainty Quantification Using Neural Networks for Molecular Property Prediction (2020) — 259 citations
• Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction (2017) — 440 citations