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Using machine learning to improve ensemble docking for drug discovery

https://doi.org/10.1002/prot.25899
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/bip.10218openalex-snapshot
The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme
inferredcites →https://doi.org/10.1002/jcc.21334openalex-snapshot
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
inferredcites →https://doi.org/10.1002/pro.5560070411openalex-snapshot
Flexible ligand docking using conformational ensembles
inferredcites →https://doi.org/10.1002/prot.20688openalex-snapshot
Molecular docking of balanol to dynamics snapshots of protein kinase A
inferredcites →https://doi.org/10.1006/jmbi.1996.0776openalex-snapshot
Molecular docking to ensembles of protein structures 1 1Edited by B. Honig
inferredcites →https://doi.org/10.1007/978-1-4939-1465-4_20openalex-snapshot
Molecular Docking to Flexible Targets
inferredcites →https://doi.org/10.1016/j.bpc.2013.10.007openalex-snapshot
Exploring the role of receptor flexibility in structure-based drug discovery
inferredcites →https://doi.org/10.1016/s0065-3233(03)66003-1openalex-snapshot
Protein Simulation and Drug Design
inferredcites →https://doi.org/10.1017/s0033583512000066openalex-snapshot
Protein flexibility in docking and surface mapping
inferredcites →https://doi.org/10.1021/acs.jcim.5b00299openalex-snapshot
Inexpensive Method for Selecting Receptor Structures for Virtual Screening
inferredcites →https://doi.org/10.1021/ci2005934openalex-snapshot
Potential and Limitations of Ensemble Docking
inferredcites →https://doi.org/10.1021/ci9003943openalex-snapshot
Recipes for the Selection of Experimental Protein Conformations for Virtual Screening
inferredcites →https://doi.org/10.1021/jm048972vopenalex-snapshot
Unveiling the Full Potential of Flexible Receptor Docking Using Multiple Crystallographic Structures
inferredcites →https://doi.org/10.1021/jm049092jopenalex-snapshot
Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
inferredcites →https://doi.org/10.1021/jm300687eopenalex-snapshot
Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
inferredcites →https://doi.org/10.1021/jp3003992openalex-snapshot
Exploring Protein Flexibility: Incorporating Structural Ensembles From Crystal Structures and Simulation into Virtual Screening Protocols
inferredcites →https://doi.org/10.1021/jp506511popenalex-snapshot
Multi-Conformer Ensemble Docking to Difficult Protein Targets
inferredcites →https://doi.org/10.1093/nar/28.1.235openalex-snapshot
The Protein Data Bank
inferredcites →https://doi.org/10.1111/j.1747-0285.2010.00996.xopenalex-snapshot
Derivatives of Salicylic Acid as Inhibitors of YopH in Yersinia pestis
inferredcites →https://doi.org/10.1142/s0219633619200013openalex-snapshot
Improving ensemble docking for drug discovery by machine learning
inferredcites →https://doi.org/10.1146/annurev.pharmtox.43.100901.140216openalex-snapshot
Protein Flexibility and Computer-Aided Drug Design
inferredcites →https://doi.org/10.1148/radiology.143.1.7063747openalex-snapshot
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
inferredcites →https://doi.org/10.1148/radiology.148.3.6878708openalex-snapshot
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
inferredcites →https://doi.org/10.1371/journal.pone.0018845openalex-snapshot
Systematic Exploitation of Multiple Receptor Conformations for Virtual Ligand Screening
inferredcites →https://doi.org/10.1517/17425255.2010.486399openalex-snapshot
Structural ensemble in computational drug screening
inferredcites →https://doi.org/10.1517/17460441.2015.1078308openalex-snapshot
Flexible receptor docking for drug discovery
inferredcites →https://doi.org/10.1517/17460441.2015.1094458openalex-snapshot
Understanding the challenges of protein flexibility in drug design
inferredcites →https://doi.org/10.2174/138161211796355056openalex-snapshot
Molecular Recognition in the Case of Flexible Targets
inferredcites →https://doi.org/10.2174/1381612811319260006openalex-snapshot
Drug Design for Protein Kinases and Phosphatases: Flexible-Receptor Docking, Binding Affinity and Specificity, and Drug-Binding Kinetics
inferredcites →https://doi.org/10.2174/156802611794863580openalex-snapshot
Accommodating Protein Flexibility for Structure-Based Drug Design
inferredcites →https://doi.org/10.3390/ijms11083016openalex-snapshot
Advances and Challenges in Protein-Ligand Docking
inferredcites →https://doi.org/10.3390/molecules20035152openalex-snapshot
Reduction of False Positives in Structure-Based Virtual Screening When Receptor Plasticity Is Considered
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Machine Learning‐Enabled Virtual Screening with Multiple Protein Structures toward the Discovery of Novel JAK3 Inhibitors: Integration of Molecular Docking, Pharmacophore, and Naïve Bayesian Classification
inferred← citeshttps://doi.org/10.1002/pro.4065openalex-snapshot
EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery
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Integration of Artificial Intelligence in Molecular Docking and Virtual Screening Methods
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inferred← citeshttps://doi.org/10.1007/s43440-022-00445-1openalex-snapshot
Opportunities and challenges in application of artificial intelligence in pharmacology
inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00001-1openalex-snapshot
Best Practices for Docking-Based Virtual Screening
inferred← citeshttps://doi.org/10.1016/bs.armc.2022.08.006openalex-snapshot
No dance, no partner! A tale of receptor flexibility in docking and virtual screening
inferred← citeshttps://doi.org/10.1016/j.biochi.2020.10.005openalex-snapshot
In silico molecular docking in DNA aptamer development
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inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00511openalex-snapshot
Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c01250openalex-snapshot
AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens
inferred← citeshttps://doi.org/10.1021/acs.jctc.3c00814openalex-snapshot
Integrated Molecular Modeling and Machine Learning for Drug Design
inferred← citeshttps://doi.org/10.1038/s41401-023-01167-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1039/d5ra05264eopenalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icmla58977.2023.00117openalex-snapshot
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3637282/v1openalex-snapshot
Evaluation of molecular docking by deep learning and Random Forests: A hybrid approach based on pseudo-convolutions
inferred← citeshttps://doi.org/10.2147/rmhp.s317735openalex-snapshot
Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia
inferred← citeshttps://doi.org/10.2174/0115748936370246250903052830openalex-snapshot
AI Data Applications in Drug Design, Development, and Diagnostics: A Comprehensive Approach to Modern Medicine
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Molecular Docking: Principles, Advances, and Its Applications in Drug Discovery
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Evaluation of Molecular Docking by Deep Learning and Random Forests: A Hybrid Approach Based on Pseudo-Convolutions
inferred← citeshttps://doi.org/10.3389/fmicb.2023.1130594openalex-snapshot
Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor
inferred← citeshttps://doi.org/10.3389/fmolb.2022.879212openalex-snapshot
Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery
inferred← citeshttps://doi.org/10.3390/diagnostics11020347openalex-snapshot
Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma
inferred← citeshttps://doi.org/10.3390/molecules27144568openalex-snapshot
Protein–Ligand Docking in the Machine-Learning Era
inferred← citeshttps://doi.org/10.3390/molecules29133158openalex-snapshot
Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides–Triazole Hybrids with Anticancer Activity
inferred← citeshttps://doi.org/10.3390/ph16091259openalex-snapshot
Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery
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