☆ 4.6 Article

The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction

JOURNAL OF CHEMINFORMATICS (2021)

Related references

Note: Only part of the references are listed.
Review Biochemical Research Methods

The impact of compound library size on the performance of scoring functions for structure-based virtual screening

Louison Fresnais et al.

Summary: Increasing the size of training datasets improves the accuracy of machine learning-based scoring functions in structure-based virtual screening, and using massive test sets can lead to fast discovery of drug leads with low-nanomolar potency. Screening larger compound libraries results in the identification of more potent actives, and ranking molecules with more accurate machine learning-based scoring functions can further enhance their potency. Additionally, classical and machine learning-based scoring functions often find different actives, suggesting the benefit of using both types of scoring functions on multiple targets.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection
Article Biochemical Research Methods

Improving structure-based virtual screening performance via learning from scoring function components

Guo-Li Xiong et al.

Summary: Scoring functions based on new protein-ligand interaction representations and advanced ML algorithms, such as the energy auxiliary terms learning (EATL) method, outperform classical SFs in terms of ROC and BEDROC, showing comparable performance with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). This approach demonstrates effectiveness in improving screening power and can be extended to other docking programs and SFs available.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection
Article Biochemical Research Methods

Extended connectivity interaction features: improving binding affinity prediction through chemical description

Norberto Sanchez-Cruz et al.

Summary: Machine-learning scoring functions have been shown to outperform standard scoring functions for predicting binding affinity of protein-ligand complexes. In this study, Extended Connectivity Interaction Features (ECIF) were introduced to describe protein-ligand complexes and create machine-learning scoring functions with improved predictions. Models built on ECIF achieved high Pearson correlation coefficients, demonstrating the descriptive power of ECIF.

BIOINFORMATICS (2021)

Add to Collection
Article Biochemical Research Methods

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Chao Shen et al.

Summary: Machine-learning-based scoring functions have shown promising results in predicting protein-ligand binding affinity and virtual screening, outperforming classical scoring functions in some aspects. However, they still fall short compared to 2D fingerprint-based QSAR models. Integrating multiple types of protein-ligand interaction features can lead to improvements, but may not surpass MACCS-based QSAR models.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection
Review Biochemistry & Molecular Biology

Recent progress on the prospective application of machine learning to structure-based virtual screening

Ghita Ghislat et al.

Summary: With the availability of more bioactivity and protein structure data, scoring functions using machine learning are becoming more accurate and widely applicable. Improvements in selecting suitable decoys and training and evaluating ML-based scoring functions have enhanced their performance for virtual screening. Recent applications have shown promising results, indicating potential for future advancements in structure-based virtual screening studies.

CURRENT OPINION IN CHEMICAL BIOLOGY (2021)

Add to Collection
Article Chemistry, Multidisciplinary

ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Xujun Zhang et al.

Summary: The ASFP platform is an online server for developing custom scoring functions for structure-based virtual screening, with modules for descriptor generation, AI-based scoring function construction, and online prediction. Through machine learning techniques, the platform enhances prediction accuracy and has been validated on benchmark datasets.

JOURNAL OF CHEMINFORMATICS (2021)

Add to Collection
Article Chemistry, Multidisciplinary

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.

Summary: This study compared the predictive capacity and computational efficiency of descriptor-based models and graph-based models for molecular property prediction. The results showed that descriptor-based models generally outperform graph-based models in terms of prediction accuracy and computational efficiency. SVM performs the best for regression tasks, while RF and XGBoost are reliable for classification tasks.

JOURNAL OF CHEMINFORMATICS (2021)

Add to Collection
Article Chemistry, Medicinal

DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures

Jingxiao Bao et al.

Summary: The machine-learning model DeepBSP is capable of predicting the root mean square deviation (rmsd) of a ligand docking pose relative to its native binding pose. Trained on a large dataset, this model demonstrates excellent docking power and is useful in accurately selecting poses closest to their native structures.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

Add to Collection
Article Chemistry, Multidisciplinary

GNINA 1.0: molecular docking with deep learning

Andrew T. McNutt et al.

Summary: Molecular docking software Gnina 1.0, utilizing convolutional neural networks as scoring functions, outperforms AutoDock Vina in redocking and cross-docking tasks when binding pockets are explicitly defined. The ensemble of CNNs shows good generalization to unseen proteins and ligands, producing scores that correlate well with known binding poses. The 1.0 version of GNINA is available under an open source license for use as a molecular docking tool.

JOURNAL OF CHEMINFORMATICS (2021)

Add to Collection
Article Biochemical Research Methods

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.

Summary: Research has shown that machine learning-based scoring functions outperform classical scoring functions in predicting protein-ligand binding affinity. Gradient boosting decision tree and random forest achieved the best predictions in most cases. The superiority of machine learning-based scoring functions is fully guaranteed when the training set contains sufficient similar targets.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection
Article Biochemical Research Methods

Beware of the generic machine learning-based scoring functions in structure-based virtual screening

Chao Shen et al.

Summary: The study found that most generic machine learning-based scoring functions perform poorly in virtual screening and may not be suitable for real virtual screening activities. Only a few exceptions, such as RFscore-VS trained on specific datasets, showed superiority for most targets.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection
Review Chemistry, Multidisciplinary

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Review Chemistry, Multidisciplinary

Machine-learning scoring functions for structure-based drug lead optimization

Hongjian Li et al.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2020)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks

Huaipan Jiang et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Add to Collection
Article Chemistry, Physical

Combined strategies in structure-based virtual screening

Zhe Wang et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2020)

Add to Collection
Article Biochemical Research Methods

Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data

Hongjian Li et al.

BIOINFORMATICS (2019)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

Random Forest Refinement of Pairwise Potentials for Protein-Ligand Decoy Detection

Jun Pei et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Add to Collection
Review Biochemistry & Molecular Biology

Molecular Docking: Shifting Paradigms in Drug Discovery

Luca Pinzi et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2019)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

Comparative Assessment of Scoring Functions: The CASF-2016 Update

Minyi Su et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Jose Jimenez et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

Add to Collection
Article Biochemical Research Methods

Boosted neural networks scoring functions for accurate ligand docking and ranking

Hossam M. Ashtawy et al.

JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (2018)

Add to Collection
Review Pharmacology & Pharmacy

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Isabella A. Guedes et al.

FRONTIERS IN PHARMACOLOGY (2018)

Add to Collection
Article Chemistry, Multidisciplinary

PotentialNet for Molecular Property Prediction

Evan N. Feinberg et al.

ACS CENTRAL SCIENCE (2018)

Add to Collection
Article Chemistry, Multidisciplinary

Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest

Cheng Wang et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2017)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

Protein-Ligand Scoring with Convolutional Neural Networks

Matthew Ragoza et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Add to Collection
Article Chemistry, Medicinal

A Simple Representation of Three-Dimensional Molecular Structure

Seth D. Axen et al.

JOURNAL OF MEDICINAL CHEMISTRY (2017)

Add to Collection
Article Multidisciplinary Sciences

Performance of machine-learning scoring functions in structure-based virtual screening

Maciej Wojcikowski et al.

SCIENTIFIC REPORTS (2017)

Add to Collection
Review Chemistry, Multidisciplinary

Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions

Zhihai Liu et al.

ACCOUNTS OF CHEMICAL RESEARCH (2017)

Add to Collection
Article Chemistry, Medicinal

Boosting Docking-Based Virtual Screening with Deep Learning

Janaina Cruz Pereira et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2016)

Add to Collection
Article Chemistry, Medicinal

Docking Screens for Novel Ligands Conferring New Biology

John J. Irwin et al.

JOURNAL OF MEDICINAL CHEMISTRY (2016)

Add to Collection
Article Chemistry, Physical

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)

Add to Collection
Article Multidisciplinary Sciences

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)

Add to Collection
Article Biochemical Research Methods

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

Hossam M. Ashtawy et al.

BMC BIOINFORMATICS (2015)

Add to Collection
Article Chemistry, Medicinal

Classification of Current Scoring Functions

Jie Liu et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Review Chemistry, Multidisciplinary

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Review Pharmacology & Pharmacy

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

Tiejun Cheng et al.

AAPS JOURNAL (2012)

Add to Collection
Article Chemistry, Medicinal

NNScore 2.0: A Neural-Network Receptor-Ligand Scoring Function

Jacob D. Durrant et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2011)

Add to Collection
Article Biochemical Research Methods

BINANA: A novel algorithm for ligand-binding characterization

Jacob D. Durrant et al.

JOURNAL OF MOLECULAR GRAPHICS & MODELLING (2011)

Add to Collection
Article Chemistry, Multidisciplinary

Open Babel: An open chemical toolbox

Noel M. O'Boyle et al.

JOURNAL OF CHEMINFORMATICS (2011)

Add to Collection
Article Biochemical Research Methods

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

Pedro J. Ballester et al.

BIOINFORMATICS (2010)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Add to Collection
Article Biochemical Research Methods

Biopython: freely available Python tools for computational molecular biology and bioinformatics

Peter J. A. Cock et al.

BIOINFORMATICS (2009)

Add to Collection
Article Biochemistry & Molecular Biology

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Chemistry, Medicinal

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)

Add to Collection
Article Biochemistry & Molecular Biology

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)

Add to Collection
Article Biochemistry & Molecular Biology

The Protein Data Bank

HM Berman et al.

NUCLEIC ACIDS RESEARCH (2000)

Add to Collection
Webinars Posters Questions Hubs Funding Journals Papers Connect Collections Reviewers About Us FAQs Mobile App Privacy Policy Terms of Use
© Peeref 2019-2024. All rights reserved.