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MotifLeadDB: A Hierarchical Structural Data Set for Congeneric Ligand Binding Activity Change
Nawoon Kim1,2, Byunghyun Bae1,3, Nuri Jung3
1Biomedical Research Division, Korea Institute of Science and Technology, Seongbuk-gu 02792, Republic of Korea.
We created MotifLeadDB, a novel structural database for drug discovery. This dataset aids in understanding structure-activity relationships and improving computational models for predicting ligand binding affinity changes.
Area of Science:
- Computational chemistry
- Structural biology
- Drug discovery
Background:
- Predicting ligand binding free energy changes from chemical substitutions is crucial for drug optimization.
- Current computational methods lack accuracy due to insufficient training data.
Purpose of the Study:
- To construct MotifLeadDB, a structural model dataset for hit-to-lead optimization.
- To provide a resource for understanding structure-activity relationships and training predictive models.
Main Methods:
- Grouped congeneric ligands by scaffolds for each receptor.
- Built protein-ligand complex structural models with ligand and side-chain optimization.
- Guided ligand docking using experimental structures with chemical similarity.
Main Results:
- Developed MotifLeadDB with 342,489 protein-ligand complex structural models.
- Organized models into 27,077 ligand scaffolds across 357 nonredundant receptors.
- Included confidence levels for all generated structural models.
Conclusions:
- MotifLeadDB facilitates direct inference of structural basis for energy changes upon substitution.
- The dataset can be used for case studies and training computational models for binding activity prediction.
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