Derek Jones
Lawrence Livermore National Laboratory
15 Papers
14 Citations
Derek Jones is an academic researcher from Lawrence Livermore National Laboratory. The author has contributed to research in topics: Computer science & Virtual screening. The author has an hindex of 5, co-authored 12 publications. Previous affiliations of Derek Jones include University of Kentucky.
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Papers
Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
Derek Jones,Hyojin Kim,Xiaohua Zhang,Adam Zemla,Garrett A. Stevenson,W. F. Drew Bennett,Daniel A. Kirshner,Sergio E. Wong,Felice C. Lightstone,Jonathan E. Allen +9 more
TL;DR: In this article, the performance of three-dimensional convolutional neural networks (3D-CNNs), spatial graph neural network (SG-CNN), and their fusion was compared to predictions based on docking scores and molecular mechanic/generalized Born surface area (MM/GBSA) calculations.
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•Posted Content
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.
Derek Jones,Hyojin Kim,Xiaohua Zhang,Adam Zemla,Garrett A. Stevenson,William D. Bennett,Dan Kirshner,Sergio E. Wong,Felice C. Lightstone,Jonathan E. Allen +9 more
TL;DR: Fusion models that combine features and inference from complementary representations to improve binding affinity prediction are presented, showing that the fusion models make more accurate predictions than their constituent neural network models as well as docking scoring and MM/GBSA rescoring, with the benefit of greater computational efficiency.
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Binding Affinity Prediction by Pairwise Function Based on Neural Network
TL;DR: A new approach to estimate the binding affinity from given three-dimensional poses of protein-ligand complexes, implemented via a neural network that takes the properties of the two atoms and their distance as input and achieves good accuracy for affinity predictions when evaluated with PDBbind 2018.
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Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning.
W. F. Drew Bennett,Stewart He,Camille L. Bilodeau,Derek Jones,Delin Sun,Hyojin Kim,Jonathan E. Allen,Felice C. Lightstone,Helgi I. Ingólfsson +8 more
TL;DR: This work runs extensive atomistic MD simulations to calculate 15,000 small molecule free energies of transfer, to train multiple ML models that predict the free energy, and shows that a spatial graph neural network model achieves the highest accuracy.
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Accelerators for Classical Molecular Dynamics Simulations of Biomolecules
Derek Jones,Jonathan E. Allen,Yue Yang,W. F. Drew Bennett,Maya Gokhale,Niema Moshiri,Tajana Rosing +6 more
TL;DR: The goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice, and provide insights into the potential of emerging hardware platforms and algorithms for MD.
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