Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

By Tiejun Cheng, Qingliang Li, Zhigang Zhou, Yanli Wang, Stephen H. Bryant. 2012

533 citations recorded by the source corpus.

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “When protein structure is available, virtual screening has traditionally relied on docking programs to predict how a compound best fits in the target's binding site and score the predicted ligand-target complex [315].”
Fast Docking on Graphics Processing Units via Ray-Casting (2013, background): “Though some docking approaches carry out sampling by greedy algorithms (such as incremental construction [38]), the most common approaches involve either individual Monte Carlo trajectories that sample Cartesian space or approaches that generate optimal solutions from a population of candidate solut”
A Structure-Based Drug Discovery Paradigm (2019, background): “Conventionally, promising-lead identification is achieved by experimental high-throughput screening (HTS), but it is time consuming and expensive [1].”
Computational Approaches in Preclinical Studies on Drug Discovery and Development (2020, methodology): “…have been widely used, for instance, to predict the binding patterns of substrates ( Macalino et al., 2015), conformational changes in enzymes (Cheng T. et al., 2012), and their catalytic eﬀects on physiological systems (Cui and Karplus, 2003), to evaluate substrate aﬃnity, instability and…”
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, background): “Currently, there is a notable shift towards scoring functions using machine learning (ML) methodologies, and this have been highlighted by several reviews (Cheng et al. , 2012; Ma et al. , 2013; Lima et al. , 2016).”
Performance of machine-learning scoring functions in structure-based virtual screening (2017, background): “We have also tested lower inactive cutoffs, also reported elsewhere 18 , but found these to be suboptimal (data not shown).”
Molecular dynamics simulations and novel drug discovery (2018, methodology): “Structure-based virtual screening based on molecular docking methods has become an indispensable tool in drug discovery [13,15,103].”
rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids (2014, background): “This is an essential step common to all successful docking-based VS undertakings [3,27].”
Ligand Pose and Orientational Sampling in Molecular Docking (2013, background): “Molecular docking is widely used to predict protein-ligand complexes[1,2] and to screen large libraries for molecules that will modulate the activity of a biological receptor.”