相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。The impact of compound library size on the performance of scoring functions for structure-based virtual screening
Louison Fresnais et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Improving structure-based virtual screening performance via learning from scoring function components
Guo-Li Xiong et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Extended connectivity interaction features: improving binding affinity prediction through chemical description
Norberto Sanchez-Cruz et al.
BIOINFORMATICS (2021)
Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
Chao Shen et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Recent progress on the prospective application of machine learning to structure-based virtual screening
Ghita Ghislat et al.
CURRENT OPINION IN CHEMICAL BIOLOGY (2021)
ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions
Xujun Zhang et al.
JOURNAL OF CHEMINFORMATICS (2021)
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Dejun Jiang et al.
JOURNAL OF CHEMINFORMATICS (2021)
DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures
Jingxiao Bao et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)
GNINA 1.0: molecular docking with deep learning
Andrew T. McNutt et al.
JOURNAL OF CHEMINFORMATICS (2021)
Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions
Chao Shen et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Beware of the generic machine learning-based scoring functions in structure-based virtual screening
Chao Shen et al.
BRIEFINGS IN BIOINFORMATICS (2021)
From machine learning to deep learning: Advances in scoring functions for protein-ligand docking
Chao Shen et al.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2020)
Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
Minyi Su et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach
Joseph A. Morrone et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Machine-learning scoring functions for structure-based drug lead optimization
Hongjian Li et al.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2020)
Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring
Wen-Ling Ye et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design
Paul G. Francoeur et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks
Huaipan Jiang et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Combined strategies in structure-based virtual screening
Zhe Wang et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2020)
Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
Hongjian Li et al.
BIOINFORMATICS (2019)
AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening
Duc Duy Nguyen et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Random Forest Refinement of Pairwise Potentials for Protein-Ligand Decoy Detection
Jun Pei et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Molecular Docking: Shifting Paradigms in Drug Discovery
Luca Pinzi et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2019)
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
Jaechang Lim et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches
Zhenxing Wu et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Comparative Assessment of Scoring Functions: The CASF-2016 Update
Minyi Su et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment
Hossam M. Ashtawy et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)
KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
Jose Jimenez et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)
Boosted neural networks scoring functions for accurate ligand docking and ranking
Hossam M. Ashtawy et al.
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (2018)
Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges
Isabella A. Guedes et al.
FRONTIERS IN PHARMACOLOGY (2018)
PotentialNet for Molecular Property Prediction
Evan N. Feinberg et al.
ACS CENTRAL SCIENCE (2018)
Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest
Cheng Wang et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2017)
Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions
Yang Li et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
Protein-Ligand Scoring with Convolutional Neural Networks
Matthew Ragoza et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
A Simple Representation of Three-Dimensional Molecular Structure
Seth D. Axen et al.
JOURNAL OF MEDICINAL CHEMISTRY (2017)
Performance of machine-learning scoring functions in structure-based virtual screening
Maciej Wojcikowski et al.
SCIENTIFIC REPORTS (2017)
Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions
Zhihai Liu et al.
ACCOUNTS OF CHEMICAL RESEARCH (2017)
Boosting Docking-Based Virtual Screening with Deep Learning
Janaina Cruz Pereira et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2016)
Docking Screens for Novel Ligands Conferring New Biology
John J. Irwin et al.
JOURNAL OF MEDICINAL CHEMISTRY (2016)
Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power
Zhe Wang et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2016)
Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery
Huiyong Sun et al.
SCIENTIFIC REPORTS (2016)
Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins
Hossam M. Ashtawy et al.
BMC BIOINFORMATICS (2015)
Classification of Current Scoring Functions
Jie Liu et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)
Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets
Hongjian Li et al.
MOLECULAR INFORMATICS (2015)
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening
Qurrat Ul Ain et al.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2015)
Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
Pedro J. Ballester et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2014)
Beware of Machine Learning-Based Scoring Functions-On the Danger of Developing Black Boxes
Joffrey Gabel et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2014)
Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise
David Ryan Koes et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2013)
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
Tiejun Cheng et al.
AAPS JOURNAL (2012)
NNScore 2.0: A Neural-Network Receptor-Ligand Scoring Function
Jacob D. Durrant et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2011)
BINANA: A novel algorithm for ligand-binding characterization
Jacob D. Durrant et al.
JOURNAL OF MOLECULAR GRAPHICS & MODELLING (2011)
Open Babel: An open chemical toolbox
Noel M. O'Boyle et al.
JOURNAL OF CHEMINFORMATICS (2011)
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking
Pedro J. Ballester et al.
BIOINFORMATICS (2010)
NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein-Ligand Complexes
Jacob D. Durrant et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)
Extended-Connectivity Fingerprints
David Rogers et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)
Biopython: freely available Python tools for computational molecular biology and bioinformatics
Peter J. A. Cock et al.
BIOINFORMATICS (2009)
Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search
Ajay N. Jain
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2007)
Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy
RA Friesner et al.
JOURNAL OF MEDICINAL CHEMISTRY (2004)
The PDBbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures
RX Wang et al.
JOURNAL OF MEDICINAL CHEMISTRY (2004)
Further development and validation of empirical scoring functions for structure-based binding affinity prediction
RX Wang et al.
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2002)
The Protein Data Bank
HM Berman et al.
NUCLEIC ACIDS RESEARCH (2000)