162 related articles for article (PubMed ID: 33593246)
1. Electrostatic Potential Energy in Protein-Drug Complexes.
Bitencourt-Ferreira G; de Azevedo Junior WF
Curr Med Chem; 2021; 28(24):4954-4971. PubMed ID: 33593246
[TBL] [Abstract][Full Text] [Related]
2. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery.
Veit-Acosta M; de Azevedo Junior WF
Curr Med Chem; 2022; 29(14):2438-2455. PubMed ID: 34365938
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS.
Bitencourt-Ferreira G; Rizzotto C; de Azevedo Junior WF
Curr Med Chem; 2021; 28(9):1746-1756. PubMed ID: 32410551
[TBL] [Abstract][Full Text] [Related]
4. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2.
Bitencourt-Ferreira G; Duarte da Silva A; Filgueira de Azevedo W
Curr Med Chem; 2021; 28(2):253-265. PubMed ID: 31729287
[TBL] [Abstract][Full Text] [Related]
5. The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity.
Veit-Acosta M; de Azevedo Junior WF
Curr Med Chem; 2021 Oct; 28(34):7006-7022. PubMed ID: 33568025
[TBL] [Abstract][Full Text] [Related]
6. Machine learning in computational docking.
Khamis MA; Gomaa W; Ahmed WF
Artif Intell Med; 2015 Mar; 63(3):135-52. PubMed ID: 25724101
[TBL] [Abstract][Full Text] [Related]
7. Electrostatic Energy in Protein-Ligand Complexes.
Bitencourt-Ferreira G; Veit-Acosta M; de Azevedo WF
Methods Mol Biol; 2019; 2053():67-77. PubMed ID: 31452099
[TBL] [Abstract][Full Text] [Related]
8. Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.
Bitencourt-Ferreira G; Villarreal MA; Quiroga R; Biziukova N; Poroikov V; Tarasova O; de Azevedo Junior WF
Curr Med Chem; 2024; 31(17):2361-2377. PubMed ID: 36944627
[TBL] [Abstract][Full Text] [Related]
9. Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.
Heck GS; Pintro VO; Pereira RR; de Ávila MB; Levin NMB; de Azevedo WF
Curr Med Chem; 2017; 24(23):2459-2470. PubMed ID: 28641555
[TBL] [Abstract][Full Text] [Related]
10. Taba: A Tool to Analyze the Binding Affinity.
da Silva AD; Bitencourt-Ferreira G; de Azevedo WF
J Comput Chem; 2020 Jan; 41(1):69-73. PubMed ID: 31410856
[TBL] [Abstract][Full Text] [Related]
11. Computational methods for calculation of ligand-binding affinity.
de Azevedo WF; Dias R
Curr Drug Targets; 2008 Dec; 9(12):1031-9. PubMed ID: 19128212
[TBL] [Abstract][Full Text] [Related]
12. Scoring functions for prediction of protein-ligand interactions.
Wang JC; Lin JH
Curr Pharm Des; 2013; 19(12):2174-82. PubMed ID: 23016847
[TBL] [Abstract][Full Text] [Related]
13. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.
Bitencourt-Ferreira G; de Azevedo WF
Biophys Chem; 2018 Sep; 240():63-69. PubMed ID: 29906639
[TBL] [Abstract][Full Text] [Related]
14. Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes.
Raha K; Merz KM
J Med Chem; 2005 Jul; 48(14):4558-75. PubMed ID: 15999994
[TBL] [Abstract][Full Text] [Related]
15. Machine Learning to Predict Binding Affinity.
Bitencourt-Ferreira G; de Azevedo WF
Methods Mol Biol; 2019; 2053():251-273. PubMed ID: 31452110
[TBL] [Abstract][Full Text] [Related]
16. ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm.
Rayka M; Karimi-Jafari MH; Firouzi R
Mol Inform; 2021 Aug; 40(8):e2060084. PubMed ID: 34021703
[TBL] [Abstract][Full Text] [Related]
17. Development of CDK-targeted scoring functions for prediction of binding affinity.
Levin NMB; Pintro VO; Bitencourt-Ferreira G; de Mattos BB; de Castro Silvério A; de Azevedo WF
Biophys Chem; 2018 Apr; 235():1-8. PubMed ID: 29407904
[TBL] [Abstract][Full Text] [Related]
18. Scoring a diverse set of high-quality docked conformations: a metascore based on electrostatic and desolvation interactions.
Camacho CJ; Ma H; Champ PC
Proteins; 2006 Jun; 63(4):868-77. PubMed ID: 16506242
[TBL] [Abstract][Full Text] [Related]
19. Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.
Liu Z; Su M; Han L; Liu J; Yang Q; Li Y; Wang R
Acc Chem Res; 2017 Feb; 50(2):302-309. PubMed ID: 28182403
[TBL] [Abstract][Full Text] [Related]
20. Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses.
Boyles F; Deane CM; Morris GM
J Chem Inf Model; 2022 Nov; 62(22):5329-5341. PubMed ID: 34469150
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
