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Literature DB >> 32049525

libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications.

Abstract

We describe libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays of voxelized molecular data. libmolgrid provides functionality for sampling batches of data suited to machine learning workflows, and it also supports temporal and spatial recurrences over that data to facilitate work with convolutional and recurrent neural networks. It was designed for seamless integration with popular deep learning frameworks and features optimized performance by leveraging graphics processing units (GPUs). libmolgrid is a free and open source project (GPLv2) that aims to democratize grid-based modeling in computational chemistry.

Entities: Chemical Disease Species

Mesh：

Year: 2020 PMID： 32049525 PMCID： PMC7500858 DOI： 10.1021/acs.jcim.9b01145

Source DB: PubMed Journal: J Chem Inf Model ISSN： 1549-9596 Impact factor: 4.956

25 in total

1. Virtual screening of molecular databases using a support vector machine.

Authors: Robert N Jorissen; Michael K Gilson
Journal: J Chem Inf Model Date: 2005 May-Jun Impact factor: 4.956

2. PlayMolecule BindScope: large scale CNN-based virtual screening on the web.

Authors: Miha Skalic; Gerard Martínez-Rosell; José Jiménez; Gianni De Fabritiis
Journal: Bioinformatics Date: 2019-04-01 Impact factor: 6.937

3. Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints.

Authors: Vladimir Chupakhin; Gilles Marcou; Igor Baskin; Alexandre Varnek; Didier Rognan
Journal: J Chem Inf Model Date: 2013-03-29 Impact factor: 4.956

4. K_DEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

Authors: José Jiménez; Miha Škalič; Gerard Martínez-Rosell; Gianni De Fabritiis
Journal: J Chem Inf Model Date: 2018-01-29 Impact factor: 4.956

5. Protein-Ligand Scoring with Convolutional Neural Networks.

Authors: Matthew Ragoza; Joshua Hochuli; Elisa Idrobo; Jocelyn Sunseri; David Ryan Koes
Journal: J Chem Inf Model Date: 2017-04-11 Impact factor: 4.956

6. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.

Authors: Alessandro Lusci; Gianluca Pollastri; Pierre Baldi
Journal: J Chem Inf Model Date: 2013-07-02 Impact factor: 4.956

5. Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry.

Authors: Hongling Zhang; Yifei Zheng
Journal: Comput Intell Neurosci Date: 2022-07-20

6. DeepFrag: a deep convolutional neural network for fragment-based lead optimization.

Authors: Harrison Green; David R Koes; Jacob D Durrant
Journal: Chem Sci Date: 2021-05-08 Impact factor: 9.825

6 in total

libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications.

1. Virtual screening of molecular databases using a support vector machine.

2. PlayMolecule BindScope: large scale CNN-based virtual screening on the web.

3. Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints.

4. K_DEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

5. Protein-Ligand Scoring with Convolutional Neural Networks.

6. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.

7. Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.

8. Optimization of Molecules via Deep Reinforcement Learning.

9. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking.

10. Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

1. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.

Review 2. Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network.

3. Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

4. Generating 3D molecules conditional on receptor binding sites with deep generative models.

5. Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry.

6. DeepFrag: a deep convolutional neural network for fragment-based lead optimization.