Pawel Siedlecki

Computational Theory and Mathematics · 2017

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 41 citations into it; #9,936 of 12,182 by trust here, #3,033 by citations
• Molecular Biology — 37 citations into it; #12,600 of 17,773 by trust here, #9,600 by citations
• Computational Chemistry & Drug Discovery — 27 citations into it
• Materials Chemistry — 21 citations into it; #7,932 of 11,513 by trust here, #9,147 by citations
• Computational Biology & Bioinformatics — 5 citations into it

Whom Pawel Siedlecki trusts overall

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

1. Pedro J. Ballester
2. Michael M. Mysinger
3. Jacob D. Durrant
4. John B. O. Mitchell
5. Marcel L. Verdonk
6. David Ryan Koes
7. George E. Dahl

Who trusts Pawel Siedlecki overall

1. Luca Pinzi

Papers from Pawel Siedlecki's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Long Short-Term Memory
3. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
4. Convolutional Networks on Graphs for Learning Molecular Fingerprints
5. Deep Learning
6. Gradient-based learning applied to document recognition
7. ImageNet classification with deep convolutional neural networks
8. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Rapid Context-Dependent Ligand Desolvation in Molecular Docking

Their papers

• Performance of machine-learning scoring functions in structure-based virtual screening (2017) — 303 citations
• Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017) — 530 citations