#11thiccs Prakash Chandra Rathi on AI for predicting molecular ESPs
ESPs v. useful for optimising lead cmpds. Shows example with electrostatic clash. Changed structure to pull electrons away from pi cloud. Much better binding.
As Astex, PLIff scoring function used a lot in VS. Knowledge-based using info from PDB. Voronoi partitioning to calculate solvent accessible areas, contact areas, contact geometries.
V. important how you type your atoms, because you calculate the propensity of an atom of one type to interact with another type.
Atom typing in PLIff is unaware of ESP around atoms. Wanted to fix/change this. Incorporating this info should improve results, hopefully.
Can approximate molecular ESP surfaces by extrema. Assign atomic features represent ESP extrema, similar to Cresset field points. Assigned lone pairs, sigma hole.
~57K molecules from eMolecules as training set. Validation set: ~5K diverse mols from ChEMBL (having reasonable activity). QM calcs, 50 days on 60 CPUs, B3LYP.
Simple lookup model is not appropriate. Shows analysis of variability depending on local environment.
....so...deep learning (ed: 1st mention today)
....so...deep learning (ed: 1st mention today)
(explaining DNNs and graph convolutional neural network) The more convolutional layers the further away atoms can influence it other. Used 6 hidden layers. 3 fully connected (FC), 3 graph convoluted (GC) and a final FC for output.
Performance on the training set. R2 is 0.96 for calculated versus predicted ESP extrema. Performance on unseen validation set: R2 is 0.88. Quite happy with this.
Work in progress: improving PLIff. Incorporate feature-feature contacts.
