SAnDReS

SAnDReS (Statistical Analysis of Docking Results and Scoring functions) draws inspiration from several protein systems. These projects began in the 1990s with pioneering studies focused on intermolecular interactions between cyclin-dependent kinase (CDK) (EC 2.7.11.22) and inhibitors (de Azevedo et al., 1996; de Azevedo et al., 1997). SAnDReS is a free and open-source (GNU General Public License) computational environment for the development of machine-learning models (Bitencourt-Ferreira & de Azevedo, 2019; Bitencourt-Ferreira et al., 2021; Bitencourt-Ferreira, Rizzotto et al., 2021) for the prediction of ligand-binding affinity (Xavier et al., 2016; Bitencourt-Ferreira & de Azevedo, 2019; Veit-Acosta & de Azevedo, 2022). We developed SAnDReS using Python programming language, and SciPy, NumPy, Scikit-Learn (Pedregosa et al., 2011), and Matplotlib libraries as a computational tool to explore the scoring function space (SFS) concept (Ross et al., 2013; Heck et al., 2017; Bitencourt-Ferreira & de Azevedo, 2019; Veríssimo et al., 2022; Bitencourt-Ferreira et al., 2024).

SAnDReS 2.0 brings together advanced tools for protein-ligand docking simulation and machine-learning modeling (de Azevedo et al., 2024 ). We have AutoDock Vina (version 1.2.3) (Eberhardt et al., 2021) as a docking engine. Also, SAnDReS 2.0 uses machine-learning methods available in the Scikit-Learn library. It has 54 regression methods, which allow us to explore the SFS. This exploration of the SFS permits us to have an adequate machine-learning model for a targeted protein system (Seifert, 2009). SAnDReS predicts binding affinity for a specific protein system with superior performance compared to classical scoring functions and other machine-learning scoring functions such as K_DEEP (Jiménez et al., 2018), CSM-lig (Pires & Ascher, 2016), and Δ_VinaRF20 (Wang & Zhang, 2017). Evaluation of predictive performance of 107 scoring functions against CASF-2016 benchmark (Su et al., 2019) indicates that a machine-learning model developed with SAnDReS 2.0 outperformed all classical and machine-learning scoring functions. In summary, SAnDReS 2.0 allows you to design a reliable scoring function adequate to the protein system of your interest.

 

How to Cite SAnDReS 2.0

de Azevedo WF Jr, Quiroga R, Villarreal MA, da Silveira NJF, Bitencourt-Ferreira G, da Silva AD, Veit-Acosta M, Oliveira PR, Tutone M, Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: Development of machine-learning models to explore the scoring function space. J Comput Chem. 2024; 45(27): 2333–2346. PubMed

 

References

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Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019; 2053: 251–273. PubMed

Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019; 2053: 275–281. PubMed

Bitencourt-Ferreira G, da Silva AD, de Azevedo WF Jr. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets. A Study of Cyclin-Dependent Kinase 2. Curr Med Chem. 2021; 28(2): 253–265. PubMed

Bitencourt-Ferreira G, Rizzotto C, de Azevedo Junior WF. Machine Learning-Based Scoring Functions. Development and Applications With SAnDReS. Curr Med Chem. 2021; 28(9): 1746–1756. PubMed

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