#11thiccs Sereina Riniker on Machine learning of partial charges from QM calcs and the applic in fixed-charge force fields and cheminf
A classical fixed-charge force field has parameters for bonded atoms and non-bonded. The non-bonded are most important for interactions. Review by me in JCIM 2018, 58, 565. Bonded are from crystallography. Charges come from QM, fitted to liquid properties.
QM-derived partial charges. Extraction from electron density is an undetermined maths problem. Most try to fit to ESP with Kollman-Singh, semi-empirical with bond order corrections, e.g. AM1-BCC. Issues, low quality QM (decrease cost), conformational dependence.
Goal a ML model to predict partial charges. Building on work on internal data from Pfizer (JCC, 2013, 34, 1661). Used 2d descriptors - no conformational dependence.
Create dataset. Select cmpds such that all substructures are present. About 130K from ZINC/ChEMBL. Focussed on organic subset, except for B. Used atom-centered atom pairs with maxlength of 2 (#rdkit). Molecules prioritized with rare substructures. All >= 4 times present.
Benchmarking versus coupled-cluster (CCSD) calcs. DDEC (density-derived electrostatic and chemical method) has low conform dependence and good benchmark results. This is a charge extraction method published in 2016.
A little error on each atom, so total doesn't add up. Spread out the excess charge but avoid making the good predictions worse by putting the excess charge onto the atom where the random forest prediction has a large prediction range (uncertainty).
Prediction accuracy. Worse for P, less data and large potential range of charges. Iodine less data but smaller range of charges so not so bad.
Works well even on small molecules , e.g. acetone, or larger ones, FDA approved drugs. Speed: not as fast as Gasteiger, but much better than semi-empirical.
Compared to reference for heat of solvation.
What can we use for in cheminf? Any ideas? Implemented Labute molecular descriptors based on these. Still preliminary. Results about the same as Gasteiger-based ones, but looking for additional applications.
What can we use for in cheminf? Any ideas? Implemented Labute molecular descriptors based on these. Still preliminary. Results about the same as Gasteiger-based ones, but looking for additional applications.
Prediction accuracy easy to extend by adding cmpds. Future work: vdW atom types should also be adjusted.
Dataset availability, see JCIM 2018, 58, 579 for the URL. Includes Python scripts to train and try it out.
Peter Ertl suggests looking at reactivity.
