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Improving Small Molecule pKa Prediction Using Transfer Learning with Graph Neural Networks
- Publication Year :
- 2022
- Publisher :
- Cold Spring Harbor Laboratory, 2022.
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Abstract
- Enumerating protonation states and calculating micro-state pKa 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 mico-state pKa predictions from molecules obtained from the ChEMBL database. The model is fine-tuned on a set of 5,994 experimental pKa 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 micro-state pKa values with high accuracy.
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi...........ba7afb05cd73056990246e99b6abb10e