Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

By David Ryan Koes, Matthew P. Baumgartner, Carlos J. Camacho. 2013

1,027 citations recorded by the source corpus.

We describe a general methodology for designing an empirical scoring function and provide smina, a version of AutoDock Vina specially optimized to support high-throughput scoring and user-specified custom scoring functions. Using our general method, the unique capabilities of smina, a set of default interaction terms from AutoDock Vina, and the CSAR (Community Structure-Activity Resource) 2010 dataset, we created a custom scoring function and evaluated it in the context of the CSAR 2011 benchmarking exercise. We find that our custom scoring function does a better job sampling low RMSD poses when crossdocking compared to the default AutoDock Vina scoring function. The design and application of our method and scoring function reveal several insights into possible improvements and the remaining challenges when scoring and ranking putative ligands.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, methodology): “We also included four popular docking methods QVina-W, GINAMcNutt et al. [2021], SMINAKoes et al. [2013] and GLIDEFriesner et al. [2004] as listed in Stärk et al. [2022].”
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022, methodology): “SMINA [17] improves Autodock Vina with a new scoring-function and user friendliness.”
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “R + S instead use SMINA for ﬁne-tuning.”
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, methodology): “Baseline method for comparison We used Smina [36], a fork of AutoDock Vina [37], as a base-line for the SB evaluation.”
Deep learning in drug discovery: an integrative review and future challenges (2022, positive): “In comparison to common docking approaches such as AutoDock Vina (Trott and Olson 2010) or Smina (Koes et al. 2013), these models have shown enhanced performance.”
DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information (2019, methodology): “However, it is worth mentioning that the ligand-based MLP also achieved passable performance, performing better than Smina.”
Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (2018, background): “Graph Autoencoder Input Layer node attribute ∈ R [480,1] Layer 1 - CNN 200 filters, max degree = 20 Layer 1 - FP Size 512 Layer 2 - CNN 100 filters, max degree = 20 Layer 2 - FP Size 512”