Role of Molecular Dynamics and Related Methods in Drug Discovery.

By Marco De Vivo, Matteo Masetti, G. Bottegoni, A. Cavalli. Journal of Medicinal Chemistry, 2016

1,134 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/04a402fd05b0faa3daa5e0620e9522da36ee5772

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Citations in the corpus

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development (2020, background): “Additional examples are also listed in the recent reviews by Cavalli and collaborators [44] and Hollingsworth and Dror [45].”
Molecular Docking: Shifting Paradigms in Drug Discovery (2019, methodology): “In silico strategies, including molecular dynamics, have also been widely used to explore the conformational space of the investigated targets, ligands, and ligand-target complexes, and thus better describing the dynamic behavior of ligand-target complexes and refining the docking results [34,40,41]”
Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations (2017, background): “…chemistry and molecular modeling techniques such as virtual screening, de novo design, and evaluation of drug-like properties play many roles in drug discovery, 20,21 here we focus on recent advances and practical considerations for determining protein − ligand binding via RBFE calculations.”
Predicting Molecular Conformation via Dynamic Graph Score Matching (2021, background): “Prior works on conformation generation mainly rely on molecular dynamics (MD) [9], where new conformations are sequentially generated based on an initial conformation and a physical model for interatomic potentials [25, 27].”
Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, background): “Enhanced sampling techniques can be divided into methods that make use of collective variables to introduce the bias and methods that do not (De Vivo et al., 2016) ( Figure 3 ).”
Learning Gradient Fields for Molecular Conformation Generation (2021, background): “Existing approaches to molecular conformation generation mainly rely on molecular dynamics (MD) (De Vivo et al., 2016), where coordinates of atoms are sequentially updated based on the forces acting on each atom.”
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles (2021, background): “…of the energy landscape and accurately sample equilibrium structures, but quickly become prohibitively slow for larger molecules [Shim and MacK-erell Jr, 2011, Ballard et al., 2015, De Vivo et al., 2016, Hawkins, 2017], e.g. they require several CPU minutes for a single drug-like molecule.”
Learning Neural Generative Dynamics for Molecular Conformation Generation (2021, methodology): “Computational approaches based on Markov chain Monte Carlo (MCMC) or molecular dynamics (MD) (De Vivo et al., 2016) are computationally expensive, especially for large molecules (Ballard et al., 2015).”