Predicting pKa using a combination of quantum mechanical and machine learning methods
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).
In this example we will illustrate how knowledge-based predictions of toxicity can be used within a MPO environment to guide the selection and design of compounds with a good balance of properties and reduced risk of toxicity.
The objective in this example is to identify one or more high quality chemistries for progression to detailed in vitro…
The models are retrained on an ad hoc basis. The models we provide are intended to cover as much of…
For the pKa models, the arrow is greyed out until you select some rows in your dataset. The pKa calculations…
Every model prediction is associated with an estimate of its confidence. You can see these by clicking on the ‘show…
All of the models in StarDrop’s ADME QSAR module are rigorously validated using external, independent test sets. You can find…
This is due to a difference in data quality. The CYP2D6 model is categorical due to an uneven distribution in…
Determine heavy atom count effortlessly with StarDrop Heavy Atom Count tool. Download now for precise molecular analysis and property estimation.
In J. Med. Chem., 2000, 43 (20), pp 3714–3717, Ertl et al. propose the calculation of two polar surface area values, the first reports the PSA…
This model calculates the “exact mass” of a molecule based upon the masses of the most abundant isotopes of its…
No, StarDrop’s ADME QSAR model will not learn from your data. However, if you want to build ADME models based…