RootedCBH_pka

RootedCBH_pka repository contains tools to run QM/ML (random forest) framework for the accurate prediction of pKas of complex organic molecules using physics-based features from DFT and structural features from our CBH fragmentation protocol. Our model corrects the functional group specific deficiencies associated with DFT and achieves impressive accuracy on two external benchmark test sets, the SAMPL6 challenge and Novartis datasets.

Link to paper: https://pubs.acs.org/doi/10.1021/acs.jcim.3c01923

Requirements

• pandas~=1.0.1

• numpy~=1.19.5

• networkx~=2.5.1

• rdkit~=2020.03.3.0

• scipy~=1.5.3

• scikit-learn~=0.24.2

• json-numpy-1.0.1

Citation

Sanchez, A. J.; Maier, S.; Raghavachari, K. Leveraging DFT and Molecular Fragmentation for Chemically Accurate p K a Prediction Using Machine Learning. J. Chem. Inf. Model. 2024, acs.jcim.3c01923. https://doi.org/10.1021/acs.jcim.3c01923