Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening

By R. Quiroga, Marcos A. Villarreal. PLoS ONE, 2016

326 citations recorded by the source corpus.

Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/a30957dedabf5297e931e6893ff07f7a644ea02a

How researchers see this paper

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In Artificial Intelligence

1. Cătălina Cangea
2. Zhewei Wei
3. Hongteng Xu
4. Zhifeng Gao
5. Hang Zheng
6. Pavol Drotár
7. Pietro Lio'
8. Arian Jamasb

In Information Systems

1. Zhewei Wei
2. Zhifeng Gao
3. Hang Zheng
4. Pietro Lio'
5. Guolin Ke

In Software

1. Zhifeng Gao
2. Hang Zheng
3. Guolin Ke

Citations in the corpus

• Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, methodology): “For the binding pose accuracy, the baselines are Autodock Vina [63, 64], Vinardo [65], 281 Smina [66], and AutoDock4 [67].”
• Structure-aware generation of drug-like molecules (2021, methodology): “We measure the Vinardo [32] docking scores of generated molecules, which was proposed as a realistic benchmark for structurebased design (SMINA benchmark [18]).”
• Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? (2023, methodology): “Molecular docking, given a ligand molecule and a ligand binding site (called ”pocket”) on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design, and several molecular docking methods [1, 2, 3, 4] are proposed in the last decades.”