4.7 Article

Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses

Related references

Note: Only part of the references are listed.
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)

Article Chemistry, Multidisciplinary

SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

Surendra Kumar et al.

Summary: In the field of drug discovery, predicting protein-ligand binding affinities accurately and quickly is crucial for optimizing lead compounds. Various machine learning or deep learning methods have been proposed to address the limitations of traditional scoring functions. While these new approaches are highly accurate, they often require complex featurization processes and additional analysis to interpret the embedded features.

JOURNAL OF CHEMINFORMATICS (2021)

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)

Article Biochemical Research Methods

Learning from the ligand: using ligand-based features to improve binding affinity prediction

Fergus Boyles et al.

BIOINFORMATICS (2020)

Editorial Material Chemistry, Medicinal

Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening

Dagmar Stumpfe et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

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)

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)

Review Chemistry, Multidisciplinary

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

Hongjian Li et al.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2020)

Article Multidisciplinary Sciences

An open-source drug discovery platform enables ultra-large virtual screens

Christoph Gorgulla et al.

NATURE (2020)

Article Biochemistry & Molecular Biology

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges

Duc Duy Nguyen et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2019)

Article Chemistry, Medicinal

In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening

Jochen Sieg et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Article Chemistry, Medicinal

Comparative Assessment of Scoring Functions: The CASF-2016 Update

Minyi Su et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Article Chemistry, Multidisciplinary

DeltaDelta neural networks for lead optimization of small molecule potency

Jose Jimenez-Luna et al.

CHEMICAL SCIENCE (2019)

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)

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)

Article Biochemistry & Molecular Biology

The ChEMBL database in 2017

Anna Gaulton et al.

NUCLEIC ACIDS RESEARCH (2017)

Review Chemistry, Multidisciplinary

Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions

Zhihai Liu et al.

ACCOUNTS OF CHEMICAL RESEARCH (2017)

Article Biochemical Research Methods

Correcting the impact of docking pose generation error on binding affinity prediction

Hongjian Li et al.

BMC BIOINFORMATICS (2016)

Article Chemistry, Medicinal

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Sereina Riniker et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)

Article Biochemistry & Molecular Biology

Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest

Hongjian Li et al.

MOLECULES (2015)

Article Chemistry, Multidisciplinary

Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field

Maciej Wojcikowski et al.

JOURNAL OF CHEMINFORMATICS (2015)

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)

Article Chemistry, Medicinal

Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results

Yan Li et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2014)

Article Chemistry, Medicinal

CSAR Benchmark Exercise 2011-2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series

Kelly L. Damm-Ganamet et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2013)

Article Chemistry, Medicinal

SFCscore(RF): A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes

David Zilian et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2013)

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)

Article Chemistry, Medicinal

Analysis of structure-based virtual screening studies and characterization of identified active compounds

Peter Ripphausen et al.

FUTURE MEDICINAL CHEMISTRY (2012)

Article Chemistry, Medicinal

Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking

Michael M. Mysinger et al.

JOURNAL OF MEDICINAL CHEMISTRY (2012)

Article Chemistry, Medicinal

CSAR Benchmark Exercise of 2010: Selection of the Protein-Ligand Complexes

James B. Dunbar et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2011)

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)

Article Chemistry, Multidisciplinary

Open Babel: An open chemical toolbox

Noel M. O'Boyle et al.

JOURNAL OF CHEMINFORMATICS (2011)

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)

Article Chemistry, Medicinal

Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets

Christian Kramer et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Article Chemistry, Medicinal

Comparative Assessment of Scoring Functions on a Diverse Test Set

Tiejun Cheng et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2009)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Biochemistry & Molecular Biology

The Protein Data Bank

HM Berman et al.

NUCLEIC ACIDS RESEARCH (2000)