Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility
Summary In this study, our researchers combined an automatic model generation process for building QSAR models with the Gaussian Processes…
The dissociation of a proton from a heteroatom has a significant influence on the charge distribution and interactions of a molecule, influencing many important molecular properties which include pharmacokinetic (PK) attributes such as solubility, tissue or cellular distribution and permeability. The ability to predict the likelihood of a molecule to lose or gain a proton in water is crucial for the development of new chemical entities with desirable PK, ADME and binding properties.
In this webinar, presented by Peter Hunt and Tamsin Mansley, we describe a new method for the prediction of the acid dissociation constant (pKa) of a heteroatom that combines quantum-mechanical and machine learning methods to generate an accurate quantitative structure-activity relationship (QSAR) model. The model achieves an R² > 0.9 and an RMSE < 1 log unit on an external test set containing both mono- and multi-protic compounds.
More predictive modelling resources
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).
Automatic generation of new compound ideas
During this example we will consider three compounds from a lead series which we would like to try to evolve into a candidate. The compound has a good profile of ADME properties but insufficient inhibition of the target, the Serotonin transporter. In this example we will use StarDrop’s Nova module to generate new ideas for compounds to improve the potency while maintaining the balance of other properties.
