maomlab/AqSolDB

Tasks

• tabular-regression

Citation text from the card

TY  - JOUR
  AU  - Sorkun, Murat Cihan
  AU  - Khetan, Abhishek
    AU  - Er, S√ºleyman
    PY  - 2019
    DA  - 2019/08/08
TI  - AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds
JO  - Scientific Data
SP  - 143
VL  - 6
IS  - 1
AB  - Water is a ubiquitous solvent in chemistry and life. 
It is therefore no surprise that the aqueous solubility of compounds has a key role in various domains, 
including but not limited to drug discovery, paint, coating, and battery materials design. 
Measurement and prediction of aqueous solubility is a complex and prevailing challenge in chemistry. 
For the latter, different data-driven prediction models have recently been developed to augment the physics-based modeling approaches. 
To construct accurate data-driven estimation models, it is essential that the underlying experimental calibration data used by these models is of high fidelity and quality.
Existing solubility datasets show variance in the chemical space of compounds covered, measurement methods, experimental conditions, 
but also in the non-standard representations, size, and accessibility of data. 
To address this problem, we generated a new database of compounds, AqSolDB, by merging a total of nine different aqueous solubility datasets, 
curating the merged data, standardizing and validating the compound representation formats, marking with reliability labels, and providing 2D descriptors of compounds as a Supplementary Resour

All datasets marked Unclear | JSON