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Improving Small Molecule pK a Prediction Using Transfer Learning With Graph Neural Networks.


ABSTRACT: Enumerating protonation states and calculating microstate pK a values of small molecules is an important yet challenging task for lead optimization and molecular modeling. Commercial and non-commercial solutions have notable limitations such as restrictive and expensive licenses, high CPU/GPU hour requirements, or the need for expert knowledge to set up and use. We present a graph neural network model that is trained on 714,906 calculated microstate pK a predictions from molecules obtained from the ChEMBL database. The model is fine-tuned on a set of 5,994 experimental pK a values significantly improving its performance on two challenging test sets. Combining the graph neural network model with Dimorphite-DL, an open-source program for enumerating ionization states, we have developed the open-source Python package pkasolver, which is able to generate and enumerate protonation states and calculate pK a values with high accuracy.

SUBMITTER: Mayr F 

PROVIDER: S-EPMC9204323 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Improving Small Molecule pK <sub><i>a</i></sub> Prediction Using Transfer Learning With Graph Neural Networks.

Mayr Fritz F   Wieder Marcus M   Wieder Oliver O   Langer Thierry T  

Frontiers in chemistry 20220526


Enumerating protonation states and calculating microstate pK <sub><i>a</i></sub> values of small molecules is an important yet challenging task for lead optimization and molecular modeling. Commercial and non-commercial solutions have notable limitations such as restrictive and expensive licenses, high CPU/GPU hour requirements, or the need for expert knowledge to set up and use. We present a graph neural network model that is trained on 714,906 calculated microstate pK <sub><i>a</i></sub> predi  ...[more]

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