Here you will find links to Jupyter Notebooks discussed in the second edition of the book Docking Screens for Drug Discovery (DOI: 10.1007/978-1-0716-4949-7). This new edition concentrates on the development of computational models to predict binding affinity based on the atomic coordinates of protein-ligand complexes. All codes are Python snippets based on the program SAnDReS 2.0 (de Azevedo et al., 2024). You will also find data about the statistical analysis of machine learning models developed using SAnDReS 2.0. As in the first edition, this book focuses on recent developments in docking simulations for target proteins with chapters on specific techniques or applications for docking simulations, including the major docking programs. Additionally, the volume explores the scoring functions developed for the analysis of docking results and to predict ligand-binding affinity as well as the importance of docking simulations for the initial stages of drug discovery. Written for the highly successful Methods in Molecular Biology series, this collection presents the kind of detail and key implementation advice to ensure successful results. You find infomation about the second edition in the following link: Docking Screens for Drug Discovery (2nd Edition).
de Azevedo WF Jr, editor. Docking screens for drug discovery. 2nd ed. New York, NY: Springer; 2026. DOI: 10.1007/978-1-0716-4949-7
da Silva AD, Veit-Acosta M, Tarasova O, de Azevedo WF Jr. A Primer on SAnDReS 2.0 for Scoring Function Design. Methods Mol Biol. 2026;2984:1-17. doi: 10.1007/978-1-0716-4949-7_1. PMID: 41075081. PubMed
Jupyter NotebooksLinearRegression4RandomData.ipynb LinearRegression4CDK2_Ki.ipynb LinearRegression4CASF-2016_Ki.ipynb LinearRegression4CDK19_IC50.ipynb LinearRegressionMultipleModels4CDK2_Ki.ipynb
da Silva AD, Baud S, de Azevedo WF Jr. Exploring the Scoring Function Space with Lasso Regression. Methods Mol Biol. 2026;2984:19-34. doi: 10.1007/978-1-0716-4949-7_2. PMID: 41075082. PubMed
Jupyter NotebooksLasso4RandomData.ipynb Lasso4CDK2_Ki.ipynb Lasso4CASF_2016_Ki.ipynb LassoRegressionMultipleModels4CASF_2016_Ki.ipynb
Pehlivan SN, da Silva AD, de Azevedo WF Jr. Combining MVD and Ridge Method to Predict CDK2 Inhibition. Methods Mol Biol. 2026;2984:35-49. doi: 10.1007/978-1-0716-4949-7_3. PMID: 41075083. PubMed
Jupyter NotebooksRidge4RandomData.ipynb Ridge_CDK2_Ki_MVD.ipynb RidgeRegressionMultipleAlphaModels4CDK2_Ki_MVD.ipynb Ridge_CDK2_Ki_Vina.ipynb RidgeRegressionMultipleModels4CDK2_Ki_MVD.ipynb
da Silva AD, de Azevedo WF Jr. Elastic Net Regression to Predict CDK2 Inhibition. Methods Mol Biol. 2026;2984:51-64. doi: 10.1007/978-1-0716-4949-7_4. PMID: 41075084. PubMed
Jupyter NotebooksElasticNet4RandomData.ipynb ElasticNet4CDK2_Ki.ipynb ElasticNet4CASF_2016_Ki.ipynb ElasticNetRegressionModels4CASF_2016_Ki.ipynb
da Silva AD, de Azevedo WF Jr. Gradient Descent to Predict Enzyme Inhibition. Methods Mol Biol. 2026;2984:65-79. doi: 10.1007/978-1-0716-4949-7_5. PMID: 41075085. PubMed
Jupyter NotebooksBGDRegressor4RandomData.ipynb SGDRegressor4RandomData.ipynb SGDRegressor4CDK2_Ki.ipynb SGDRegressor4CASF_2016_Ki.ipynb SGDRegressorModels4CASF_2016_Ki.ipynb
da Silva AD, de Azevedo WF Jr. Decision Tree for Prediction of Binding Affinity. Methods Mol Biol. 2026;2984:81-95. doi: 10.1007/978-1-0716-4949-7_6. PMID: 41075086. PubMed
Jupyter NotebookSKReg4Model.ipynb
da Silva AD, de Azevedo WF Jr. Calculating Enzyme Inhibition with Random Forests. Methods Mol Biol. 2026;2984:97-110. doi: 10.1007/978-1-0716-4949-7_7. PMID: 41075087. PubMed
Jupyter NotebooksMVD4ML.ipynb SKReg4Model.ipynb
da Silva AD, de Azevedo WF Jr. Extremely Randomized Trees to Determine Binding Affinity. Methods Mol Biol. 2026;2984:111-123. doi: 10.1007/978-1-0716-4949-7_8. PMID: 41075088. PubMed
Jupyter NotebooksMVD4ML.ipynb SKReg4Model.ipynb
Dere D, Pehlivan SN, da Silva AD, de Azevedo WF Jr. Hands-On Docking with Molegro Virtual Docker. Methods Mol Biol. 2026;2984:125-138. doi: 10.1007/978-1-0716-4949-7_9. PMID: 41075089. PubMed
Jupyter NotebooksMVD4ML.ipynb SKReg4Model.ipynb
Oliveira JMV, da Silva AD, Soares AMDS, de Azevedo WF Jr. Molegro Virtual Docker for Docking Screens. Methods Mol Biol. 2026;2984:139-152. doi: 10.1007/978-1-0716-4949-7_10. PMID: 41075090. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb csv4metrics.ipynb
da Silva AD, da Silveira NJF, Oliveira PR, de Azevedo WF Jr. Molegro Data Modeller for Machine Learning. Methods Mol Biol. 2026;2984:153-166. doi: 10.1007/978-1-0716-4949-7_11. PMID: 41075091. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb csv4metrics.ipynb
da Silva AD, de Azevedo WF Jr. Neural Networks with Molegro Data Modeller. Methods Mol Biol. 2026;2984:167-181. doi: 10.1007/978-1-0716-4949-7_12. PMID: 41075092. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb csv4metrics.ipynb
da Silva AD, de Azevedo WF Jr. AlphaFold for Docking Screens. Methods Mol Biol. 2026;2984:183-196. doi: 10.1007/978-1-0716-4949-7_13. PMID: 41075093. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb visualize_dataset.ipynb csv4metrics.ipynb
da Silva AD, Russo S, González-Vergara E, de Azevedo WF Jr. Differential Evolution for Docking Simulations. Methods Mol Biol. 2026;2984:197-210. doi: 10.1007/978-1-0716-4949-7_14. PMID: 41075094. PubMed
Jupyter NotebookDarwin.ipynb
de Azevedo WF Jr. Machine Learning to Predict CDK4 Inhibition. Methods Mol Biol. 2026;2984:211-225. doi: 10.1007/978-1-0716-4949-7_15. PMID: 41075095. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb visualize_dataset.ipynb csv4metrics.ipynb
de Azevedo WF Jr. Targeting CDK9 with Molegro Virtual Docker. Methods Mol Biol. 2026;2984:227-242. doi: 10.1007/978-1-0716-4949-7_16. PMID: 41075096. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb visualize_dataset.ipynb csv4metrics.ipynb
de Azevedo WF Jr. CDK7 as a Target for Docking Screens. Methods Mol Biol. 2026;2984:243-258. doi: 10.1007/978-1-0716-4949-7_17. PMID: 41075097. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb visualize_dataset.ipynb csv4metrics.ipynb
de Azevedo WF Jr. Molegro Data Modeller to Estimate CDK6 Inhibition. Methods Mol Biol. 2026;2984:259-275. doi: 10.1007/978-1-0716-4949-7_18. PMID: 41075098. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb visualize_dataset.ipynb csv4metrics.ipynb
de Azevedo WF Jr. Neural Networks to Calculate CDK2 Inhibition. Methods Mol Biol. 2026;2984:277-293. doi: 10.1007/978-1-0716-4949-7_19. PMID: 41075099. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb visualize_dataset.ipynb csv4metrics.ipynb
de Azevedo WF Jr. Tree-Based Methods to Predict Enzyme Inhibition. Methods Mol Biol. 2026;2984:295-311. doi: 10.1007/978-1-0716-4949-7_20. PMID: 41075100. PubMed
Jupyter Notebooksprepare_BindingDB.ipynb prepare_MVD.ipynb visualize_dataset.ipynb SKReg4Model.ipynbAdditional Software (Including Third-Party Software)
AutoDock-Vina (1.2.7) (Linux Version) AutoDock-Vina (1.2.7) (Windows Version) AutoDock-Vina Split (1.2.7) (Linux Version) AutoDock-Vina Split (1.2.7) (Windows Version) SAnDReS 2.0 (Linux Version) Taba (Linux Version)
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
de Azevedo WF Jr. Docking screens for drug discovery. 1st ed. de Azevedo WF Jr, editor. New York, NY: Humana Press; 2020. DOI: 10.1007/978-1-4939-9752-7