scoring-function-space

There are 0 repository under scoring-function-space topic.

• sandres

  azevedolab / sandres

  SAnDReS (Statistical Analysis of Docking Results and Scoring functions) is a free and open-source computational environment for the development of machine-learning models for the prediction of ligand-binding affinity. We developed SAnDReS using Python programming language, and SciPy, NumPy, scikit-learn, and Matplotlib libraries as a computational

  sandresdockingprotein-ligandpythonimachine-learningprotein-drug-interactionsscikit-learnscoring-function-space
  Language:Python 14

• azevedolab / taba

  Repository for the Taba tool

  binding-affinityprotein-ligand-dockingprotein-ligand-interactionsscoring-function-spacemachine-learningmass-spring-systemspythonscikit-learn
  Language:Python 7

• azevedolab / SFSXplorer

  Computational tool to explore the scoring fucntion space

  dockingmachine-learningpythonscikit-learnproteinprotein-drug-interactionsprotein-structurescoring-functionsscoring-function-space
  Language:Python 3

• azevedolab / emp_free_energy_FF_AD4

  Empirical Free Energy Force Field for AutoDock 4 Overview AutoDock 4 estimates free energy of binding for a receptor-ligand complex using a semi-empirical free energy force field. This force field has been calibrated against a dataset composed of crystallographic structures for which ligand-binding affinity data is known (Morris et al., 2009). The present Python code calculates the van der Waals, intermolecular hydrogen bond, electrostatic interaction, and desolvation potentials based on the atomic coordinates of the ligand and the receptor. This program reads atomic coordinates in the PDBQT format and prints the potential energy terms. It is not calibrated for a specific dataset, so it might be used to develop targeted-scoring functions, which may be used to explore the scoring function space (Heck et al. 2017). The zipped folder has the atomic coordinates for both, receptor (receptor.pdbqt) and ligand (lig.pdbqt) structures. I intend to use this code to develop a new tool in the SAnDReS program (Xavier et al., 2016)( https://github.com/azevedolab/sandres) to generate targeted-scoring functions. References Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459–2470. Morris GM, Huey R, Lindstrom W, Sanner, MF, Belew RK, Goodsell DS, Olson AJ. Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 2009 30: 2785–2791. Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016; 19(10): 801–812.

  machine-learningprotein-ligand-interacionsscoring-function-space
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