MedusaScore: An Accurate Force-Field Based Scoring Function for Virtual Drug Screening

By Shuangye Yin, Lada Biedermannová, Jirí Vondrásek, N. V. Dokholyan. 2008

201 citations recorded by the source corpus.

Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Shengyou Huang
2. Xiaoqin Zou
3. N. V. Dokholyan
4. Jian Wang
5. Alexander Tropsha
6. Elizabeth A. Proctor
7. Shuangye Yin
8. Yumeng Yan

In Materials Chemistry

1. N. V. Dokholyan
2. Shengyou Huang
3. Jian Wang
4. Shuangye Yin
5. Alexander Tropsha
6. Elizabeth A. Proctor
7. Xiaoqin Zou
8. Yumeng Yan

In Molecular Biology

1. Shengyou Huang
2. Xiaoqin Zou
3. N. V. Dokholyan
4. Jian Wang
5. Shuangye Yin
6. Alexander Tropsha
7. Elizabeth A. Proctor
8. Yumeng Yan

In Computational Chemistry & Drug Discovery

1. Shengyou Huang
2. Xiaoqin Zou
3. N. V. Dokholyan
4. Alexander Tropsha
5. Stefano Moro
6. Veronica Salmaso

Citations in the corpus

• MedusaDock 2.0: Efficient and Accurate Protein-Ligand Docking With Constraints (2019, background): “In order to test the performance of MedusaScore, we evaluate the interaction energies of MedusaDock 2.0 generated Endothiapepsin-ritonavir complexes using MedusaScore.”
• Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets. (2012, methodology): “function (20) to dock and score the native ligands with their respective”
• Advances and Challenges in Protein-Ligand Docking (2010, background): “GlideScore [51,52], PLP [119,120], SYBYL/F-Score [55], LigScore [118], LUDI [115,117], SCORE [116], X-Score [121], ChemScore [114], MedusaScore [122], AIScore [123], and SFCscore [124] are examples of empirical scoring functions.”