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ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Global Free Energy Scoring Functions Based on Distance-Dependent Atom-Type Pair Descriptors
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Ligand Identification Scoring Algorithm (LISA)
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Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries
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Statistical Learning Theory
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Improving structure-based virtual screening performance via learning from scoring function components
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Computationally predicting binding affinity in protein–ligand complexes: free energy-based simulations and machine learning-based scoring functions
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Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction
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A point cloud-based deep learning strategy for protein–ligand binding affinity prediction
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Prediction of protein–ligand binding affinity via deep learning models
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Building a virtual ligand screening pipeline using free software: a survey
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Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions
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KORP-PL: a coarse-grained knowledge-based scoring function for protein–ligand interactions
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Extended connectivity interaction features: improving binding affinity prediction through chemical description
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Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity
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PDB-wide collection of binding data: current status of the PDBbind database
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PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation
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Convex-PL R – Revisiting affinity predictions and virtual screening using physics-informed machine learning
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Identification of Catechins Binding Pockets in Monomeric A β 42 Through Ensemble Docking and MD Simulations
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Structure-Based Protein-Drug Affinity Prediction with Spatial Attention Mechanisms
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TS ‐Chemscore, a Target‐Specific Scoring Function, Significantly Improves the Performance of Scoring in Virtual Screening
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A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina
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Discovery of selective protein arginine methyltransferase 5 inhibitors and biological evaluations
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Structure‐based discovery of new selective small‐molecule sirtuin 5 inhibitors
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Multitask deep networks with grid featurization achieve improved scoring performance for protein–ligand binding
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Exploring the Potential of Calebin‐A in Targeting Obesity‐Related Genes and Pathways
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Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
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Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study
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Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
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Docking-based virtual screening of TβR1 inhibitors: evaluation of pose prediction and scoring functions
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DLIGAND2: an improved knowledge-based energy function for protein–ligand interactions using the distance-scaled, finite, ideal-gas reference state
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SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors
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GNINA 1.0: molecular docking with deep learning
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Structure-based, deep-learning models for protein-ligand binding affinity prediction
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Building shape-focused pharmacophore models for effective docking screening
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istar: A Web Platform for Large-Scale Protein-Ligand Docking
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The Augmenting Effects of Desolvation and Conformational Energy Terms on the Predictions of Docking Programs against mPGES-1
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27Chapter 2 AI-based approaches to cancer drug discovery
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Soft Computing Tools for Virtual Drug Discovery
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An overview of recent advances and challenges in predicting compound-protein interaction (CPI)
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Computational methods for calculation of protein-ligand binding affinities in structure-based drug design
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Support vector machines for drug discovery
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Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery
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Virtual Screening Meets Deep Learning
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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
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Molecular Docking of Michelia alba Leaves Active Compounds Against Human Epidermal Growth Factor Receptor 2 (HER-2)
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GNINA 1.0: Molecular Docking with Deep Learning
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Structure-Based Virtual Screening: From Classical to Artificial Intelligence
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RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features
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Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges
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Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers
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To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches
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Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods
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Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications
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Key Topics in Molecular Docking for Drug Design
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Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review
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Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features
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Identification of Catechins’ Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations
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A Practical Guide to Computational Tools for Engineering Biocatalytic Properties
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Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest
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Methods for Docking and Drug Designing
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Scoring Functions in Docking Experiments
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Scoring Functions of Protein-Ligand Interactions
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Methods for Docking and Drug Designing
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Scoring Functions of Protein-Ligand Interactions
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Machine Learning and Deep Learning in Data-Driven Decision Making of Drug Discovery and Challenges in High-Quality Data Acquisition in the Pharmaceutical Industry
inferred← citeshttps://doi.org/10.48550/arxiv.1703.10982openalex-snapshot
Topological fingerprints reveal protein-ligand binding mechanism
inferred← citeshttps://doi.org/10.48550/arxiv.1704.05883openalex-snapshot
Rigidity strengthening is a vital mechanism for protein-ligand binding
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Algebraic graph learning of protein-ligand binding affinity
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Additional file 1 of SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors
inferred← citeshttps://doi.org/10.6084/m9.figshare.14759382openalex-snapshot
Additional file 1 of GNINA 1.0: molecular docking with deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26660296openalex-snapshot
Additional file 1 of Structure-based, deep-learning models for protein-ligand binding affinity prediction
inferred← citeshttps://doi.org/10.70675/2b288725z2446z48c5z833ezf53915cd324bopenalex-snapshot
Développement d’outils statistiques d’évaluation de méthodes de criblage virtuel : courbes de prédictivité & Screening Explorer
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Recherche d'inhibiteurs de l'interaction Lutheran-Laminine par des techniques de modélisation et de simulation moléculaires
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Docking et Deep Learning : affinement de méthodologies in silico pour le développementde stratégies thérapeutiques innovantes
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Development of novel algorithms for data-driven prediction of interactions between proteins and small molecules
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