Protein-Ligand Scoring with Convolutional Neural Networks

By Matthew Ragoza, Joshua E. Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes. 2017

465 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Tommi S. Jaakkola
2. Christopher R'e
3. Michael M. Bronstein
4. Regina Barzilay
5. Luc Van Gool
6. Kevin P. Murphy
7. Chunhua Shen
8. Dacheng Tao

In Computational Chemistry & Drug Discovery

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Hannes Stärk
4. Jörg Kurt Wegner
5. Olexandr Isayev
6. Ron O. Dror
7. Vignesh Ram Somnath
8. E. A. Del Rio-Chanona

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “A 3D grid can featurize the input complex [34,319].”
• EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “…fine-tuning of predicted docked poses, as well as direct binding affinity prediction from the 3D complex (McNutt et al., 2021; Fran-coeur et al., 2020; Ragoza et al., 2017; Wallach et al., 2015; Lim et al., 2019; Morrone et al., 2020; Jiang et al., 2021; Shen et al., 2021; Jastrzebski et al., 2020;…”
• Multi-Scale Representation Learning on Proteins (2022, methodology): “3D convolutional neural networks have been utilized for protein quality assessment [Derevyanko et al., 2018], protein contact prediction [Townshend et al., 2019] and protein-ligand binding affinity tasks [Ragoza et al., 2017, Jiménez et al., 2018, Townshend et al., 2020].”
• Utilizing graph machine learning within drug discovery and development (2021, methodology): “The authors report improved performance against the previous SOTA model based on a 3D-CNN operating on a grid-structure representation of the protein pocket [213].”
• Outsmarting Quantum Chemistry Through Transfer Learning (2018, background): “Recent chemistry related work reports on ML models for chemical reactions 21,22 , potential energy surfaces 23–27 , forces 28–30 , atomization energies 31,32 , atomic partial charges 32– 35 , molecular dipoles 26,36,37 , materials discovery 38–40 , and protein-ligand complex scoring 41 .”
• Machine Learning on Graphs: A Model and Comprehensive Taxonomy (2020, background): “conductance and malleability), and others are concerned drug design (Jin et al., 2018; Ragoza et al., 2017; Feng et al., 2018).”