DockTData brings structural and energetic records into one consistent, research-ready foundation. Explore the dataset.
01
FAIR-compliant
Findable, accessible, interoperable, and reusable data practices support transparent open science.
02
Bioactivity parameters
Ki, Kd, IC50, and EC50 values use normalized units for consistent comparison across experiments.
03
Machine-learning ready
Structured records support predictive modeling and deep-learning workflows without unnecessary preparation overhead.
04
Multi-source integration
Protein Data Bank, BindingDB, and ChEMBL records are brought together through a reproducible pipeline.
05
Quality validated
Preparation and validation criteria help maintain reliable structural and experimental relationships.
06
Discovery focused
The dataset supports rational drug design, virtual screening, and broader molecular-modeling studies.
02 Method
Bridging structure and bioactivity.
DockTData is an automated extract, transform, and load pipeline designed to connect structural biology with molecular energetics.
Experimental structures from the Protein Data Bank are synchronized with bioactivity data from BindingDB and ChEMBL. The resulting receptor–ligand records are prepared for scalability, reproducibility, and use across machine-learning and molecular-modeling studies.
The Grupo de Modelagem Molecular de Sistemas Biológicos at Laboratório Nacional de Computação Científica develops computational tools for molecular modeling and drug discovery research.
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