In any task in the realm of virtual screening, there need to be many filters applied to a dataset of ligands to downselect the ‘best’ ones on a number of parameters to produce a manageable size. One popular filter is if a compound has a physical pose and good affinity as predicted by tools such as docking or energy minimisation. In my pipeline for downselecting elaborations of compounds proposed as fragment follow-ups, I calculate the pose and ΔΔG by energy minimizing the ligand with atom restraints to matching atoms in the fragment inspiration. I either use RDKit using its MMFF94 forcefield or PyRosetta using its ref2015 scorefunction, all made possible by the lovely tool Fragmenstein.
With RDKit as the minimizer the protein neighborhood around the ligand is fixed and placements take on average 21s whereas with PyRosetta placements, they take on average 238s (and I can run placements in parallel luckily). I would ideally like to use RDKit as the placement method since it is so fast and I would like to perform 500K within a few days but, I wanted to confirm that RDKit is ‘good enough’ compared to the slightly more rigorous tool PyRosetta (it allows residues to relax and samples more conformations with the longer runtime I think).
Category Archives: Protein-Ligand Docking
Fine-tune generated molecular poses with a force field
Some molecular pose generation methods benefit from an energy relaxation post-processing step.
Here is a quick way to do this using OpenMM via a short script I prepared:
Continue readingDemystifying the thermodynamics of ligand binding
Chemoinformatics uses a curious jumble of terms from thermodynamics, wet-lab techniques and statistical terminology, which is at its most jarring, it could be argued, in machine learning. In some datasets one often sees pIC50, pEC50, pKi and pKD, in discussion sections a medchemist may talk casually of entropy, whereas in the world of molecular mechanics everything is internal energy. Herein I hope to address some common misconceptions and unify these concepts.
Continue readingA simple criterion can conceal a multitude of chemical and structural sins
We’ve been investigating deep learning-based protein-ligand docking methods which often claim to be able to generate ligand binding modes within 2Å RMSD of the experimental one. We found, however, this simple criterion can conceal a multitude of chemical and structural sins…
DeepDock attempted to generate the ligand binding mode from PDB ID 1t9b
(light blue carbons, left), but gave pretzeled rings instead (white carbons, right).
9th Joint Sheffield Conference on Cheminformatics
Over the next few days, researchers from around the world will be gathering in Sheffield for the 9th Joint Sheffield Conference on Cheminformatics. As one of the organizers (wearing my Molecular Graphics and Modeling Society ‘hat’), I can say we have an exciting array of speakers and sessions:
- De Novo Design
- Open Science
- Chemical Space
- Physics-based Modelling
- Machine Learning
- Property Prediction
- Virtual Screening
- Case Studies
- Molecular Representations
It has traditionally taken place every three years, but despite the global pandemic it is returning this year, once again in person in the excellent conference facilities at The Edge. You can download the full programme in iCal format, and here is the conference calendar:
Continue readingLigands of CASF-2016
CASF-2016 is a commonly used benchmark for docking tools. Unfortunately, some of the provided ligand files cannot be loaded using RDKit (version 2022.09.1) but there is an easy remedy.
Continue readingMeeko: Docking straight from SMILES string
When docking, using software like AutoDock Vina, you must prepare your ligand by protonating the molecule, generating 3D coordinates, and converting it to a specific file format (in the case of Vina, PDBQT). Docking software typically needs the protein and ligand file inputs to be written on disk. This is limiting as generating 10,000s of files for a large virtual screen can be annoying and hinder the speed at which you dock.
Fortunately, the Forli group in Scripps Research have developed a Python package, Meeko, to prepare ligands directly from SMILES or other molecule formats for docking to AutoDock 4 or Vina, without writing any files to disk. This means you can dock directly from a single file containing all the SMILES of the ligands you are investigating!
Continue reading5th Artificial Intelligence in Chemistry Symposium
The lineup for the Royal Society of Chemistry’s 5th “Artificial Intelligence in Chemistry” Symposium (Thursday-Friday, 1st-2nd September 2022) is now complete for both oral and poster presentations. It really is a fantastic selection of topics and speakers and it is clear this event is now a highlight of the scientific calendar. Our very own Prof. Charlotte M. Deane, MBE will be giving a keynote.
It marks a return to in-person meetings: it will be held at Churchill College, Cambridge, with a conference dinner at Trinity Hall.
More details are here: https://www.rscbmcs.org/events/aichem22/.
Registration for in person attendance is open until Monday 29th August 17:00 (BST).
It is also possible to register for virtual attendance; the meeting will be broadcast on Zoom.
Paper review: “EquiBind”
Molecular docking helps us understand how small-molecules interact with proteins. This is especially useful in early drug development stages such as target identification and compound screening. Quick and accurate docking software allows researchers to focus their attention on a smaller set of lead molecules for further testing. Traditionally, docking software has employed first principles from physics and chemistry. Recently, deep learning has become all the rage for molecular docking, maybe motivated by the successful application of deep learning to molecular folding.
Method
EquiBind is a deep learning unconstrained docking method which models a fixed receptor and a ligand with selected rotatable bonds. It predicts the binding pocket and the ligand’s conformation within the pocket in one go. Under the hood, EquiBind employs two great ideas from a recent ICLR 2022 Paper: a SE3-invariant graph neural network based architecture and the idea to generate fixed sets of matching key points to define a rotation and translation between receptor and ligand. In addition, the authors innovate a fast method to project a deformed ligand onto the space spanned by the rotatable bonds of a pre-generated ligand conformation.
Continue readingHow to prepare a molecule for RDKit
RDKit is very fussy when it comes to inputs in SDF format. Using the SDMolSupplier, we get a significant rate of failure even on curated datasets such as the PDBBind refined set. Pymol has no such scruples, and with that, I present a function which has proved invaluable to me over the course of my DPhil. For reasons I have never bothered to explore, using pymol to convert from sdf, into mol2 and back to sdf format again (adding in missing hydrogens along the way) will almost always make a molecule safe to import using RDKit:
from pathlib import Path
from pymol import cmd
def py_mollify(sdf, overwrite=False):
"""Use pymol to sanitise an SDF file for use in RDKit.
Arguments:
sdf: location of faulty sdf file
overwrite: whether or not to overwrite the original sdf. If False,
a new file will be written in the form <sdf_fname>_pymol.sdf
Returns:
Original sdf filename if overwrite == False, else the filename of the
sanitised output.
"""
sdf = Path(sdf).expanduser().resolve()
mol2_fname = str(sdf).replace('.sdf', '_pymol.mol2')
new_sdf_fname = sdf if overwrite else str(sdf).replace('.sdf', '_pymol.sdf')
cmd.load(str(sdf))
cmd.h_add('all')
cmd.save(mol2_fname)
cmd.reinitialize()
cmd.load(mol2_fname)
cmd.save(str(new_sdf_fname))
return new_sdf_fname