Difference between revisions of "Projects/CASF: Comparative assessment of scoring functions"
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| − | =Problem 53= | + | {{#seo: |
| + | |title=Comparative assessment of scoring functions | ||
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| + | |keywords=Comparative assessment of scoring functions | ||
| + | |description=Projects/CASF: Comparative assessment of scoring functions for protein-ligand docking problem | ||
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| + | ==Problem 53== | ||
* Title: CASF: Comparative assessment of scoring functions for protein-ligand docking problem | * Title: CASF: Comparative assessment of scoring functions for protein-ligand docking problem | ||
| − | * Problem: We have to create a scoring model^ which predicts will the couple protein and ligand be | + | * Problem: We have to create a scoring model^ which predicts will the couple protein and ligand be complex. |
| − | * Data: The project code | + | * Data: The project code contains two parts. The first one is [https://github.com/Intelligent-Systems-Phystech/ProbabilisticMetricSpaces ProbalilisticMetricSpaces], and the second one is in a notebook, by request. |
* References: | * References: | ||
*# Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization by Maria Kadukova and Sergei Grudinin, 2017[https://link.springer.com/article/10.1007/s10822-017-0068-8] | *# Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization by Maria Kadukova and Sergei Grudinin, 2017[https://link.springer.com/article/10.1007/s10822-017-0068-8] | ||
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*# Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4 Maria Kadukova et al., 2020[https://hal.archives-ouvertes.fr/hal-02434514] | *# Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4 Maria Kadukova et al., 2020[https://hal.archives-ouvertes.fr/hal-02434514] | ||
* Basic solution: The solution is a linear combination, it is described in [https://hal.inria.fr/hal-01591154/ this paper], and [https://www.overleaf.com/project/5bdc5b1a7caa0c4c233790ed draft]. | * Basic solution: The solution is a linear combination, it is described in [https://hal.inria.fr/hal-01591154/ this paper], and [https://www.overleaf.com/project/5bdc5b1a7caa0c4c233790ed draft]. | ||
| − | * Method: We propose a novel approach to reconstruct distribution of bonding angles and solve the scoring problem as a problem of metric classification. | + | * Method: We propose a novel approach to reconstruct the distribution of bonding angles and solve the scoring problem as a problem of metric classification. |
* Authors: Sergei Grudinin, Maria Kadukova, and Vadim Strijov | * Authors: Sergei Grudinin, Maria Kadukova, and Vadim Strijov | ||
=Current goals= | =Current goals= | ||
Latest revision as of 00:14, 17 February 2024
Problem 53
- Title: CASF: Comparative assessment of scoring functions for protein-ligand docking problem
- Problem: We have to create a scoring model^ which predicts will the couple protein and ligand be complex.
- Data: The project code contains two parts. The first one is ProbalilisticMetricSpaces, and the second one is in a notebook, by request.
- References:
- Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization by Maria Kadukova and Sergei Grudinin, 2017[1]
- Predicting Binding Poses and Affinities in the CSAR 2013–2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential by Sergei Grudinin et al, 2016[2]
- Comparative Assessment of Scoring Functions: The CASF-2016 Update by Minyi Su et al., 2018[3]
- Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4 Maria Kadukova et al., 2020[4]
- Basic solution: The solution is a linear combination, it is described in this paper, and draft.
- Method: We propose a novel approach to reconstruct the distribution of bonding angles and solve the scoring problem as a problem of metric classification.
- Authors: Sergei Grudinin, Maria Kadukova, and Vadim Strijov
Current goals
- To run the basic code with slight model variations and repeat the computational experiment
- To combine the probability distributions with the basic code
- To develop the model and beat the tests