Protein–ligand docking: Current status and future challenges

By Sérgio Filipe Sousa, Pedro Alexandrino Fernandes, Maria João Ramos. 2006

939 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry, Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, methodology): “Systematic and stochastic methods are included in widely used molecular docking programs, which have specific approaches to address their respective problems [27].”
End-to-end protein–ligand complex structure generation with diffusion-based generative models (2022, background): “Despite its success in drug discovery and other applications, correct sampling of binding poses can be limited by poor modeling of protein ﬂexibility [9–11].”
Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get” (2009, neutral): “…mechanics with PoissonBoltzmann + surface area or MM-GBSA (GB stands for Generalized Born), and the method was originated by the Kollman and Case labs in the late 1990s (Cheatham et al., 1998; Kollman et al., 2000; Srinivasan et al., 1998), with parallel work by others (Vorobjev and Hermans, 1999).”
Interactions between milk proteins and polyphenols: Binding mechanisms, related changes, and the future trends in the dairy industry (2018, background): “protein and the ligand as a rigid body or inclusion of the flexibility of the ligand while the protein remains as a rigid body, with the latter being more commonly preferred for protein–polyphenol docking studies.([49,76,95,97])”
An interaction-motif-based scoring function for protein-ligand docking (2010, methodology): “…in G-Score [10,11] or AutoDock 3.0 [12] is based on the molecular mechanics force field used in Tripos [13] or Amber [14], respectively, and F-Score [15] and ChemScore [16] derive the coefficients of their energy terms using regression analysis of experimentally determined binding energies [17].”
High quality binding modes in docking ligands to proteins (2008, methodology): “24 The docking process may be performed using simulated annealing (SA), genetic algorithm (GA), or Lamarckian genetic algorithm (LGA).”
Virtual Screening with AutoDock: Theory and Practice (2010, background): “Building on 2 decades of research, many successful methods for docking of ligands to macromolecular targets have been developed [6-13].”
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “As a crucial stage in drug discovery, predicting protein-ligand binding affinity has been intensively studied for a long time [11, 34], which is of great importance for efficient and accurate drug screening.”
Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. (2013, background): “Naturally, the two critical elements in a search algorithm are speed and effectiveness in covering the relevant conformational space [1].”
A Structure-Based Drug Discovery Paradigm (2019, methodology): “The limitations of the scoring functions are a major drawback among docking programs because this software provides an efficient evaluation of ligand binding energy but ignores accuracy [52].”
Sub‐angstrom modeling of complexes between flexible peptides and globular proteins (2010, background): “First, peptides in general contain significantly more rotatable bonds than small molecules,30 raising the bar for computational efforts to model the interactions accurately and efficiently.”
Computational Methods in Drug Discovery (2014, background): “The probability of acceptance of an uphill step decreases with increasing energy gap and depends on the "temperature" of the MCM simulation (Sousa et al., 2006).”