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).
When showcasing an approach in computational chemistry, an example molecule is required as a placeholder. But which to chose from? I would classify there different approaches: choosing a recognisable molecules, a top selling drugs, or a randomly sketched compound.
At a recent conference, Sheffield Cheminformatics 2023, I saw examples of all three and one problem I had that some placeholders distracted me into searching to figure out what it was.
Continue readingOver 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:
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 readingFinding the parts in common between two molecules appears to be a straightforward, but actually is a maze of layers. The task, maximum common substructure (MCS) searching, in RDKit is done by Chem.rdFMCS.FindMCS, which is highly customisable with lots of presets. What if one wanted to control in minute detail if a given atom X and is a match for atom Y? There is a way and this is how.
I thought I’d share a list of some of the other blogs that have helped me during my PhD so far and may be useful to new starters (or those who may not have come across them before). This list is by no means exhaustive and I’m very open to other recommendations!
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Deep learning (DL) methods in structural modelling are outcompeting force fields because they overcome the two main limitations to force fields methods – the prohibitively large search space for large systems and the limited accuracy of the description of the physics [4].
However, the two methods are also compatible. DL methods are helping to close the gap between the applications of force fields and ab initio methods [3]. The advantage of DL-based force fields is that the functional form does not have to be specified explicitly and much more accurate. Say goodbye to the 12-6 potential function.
In principle DL-based force fields can be applied anywhere where regular force fields have been applied, for example conformation generation [2]. The flip-side of DL-based methods commonly is poor generalization but it seems that force fields, when properly trained, generalize well. ANI trained on molecules with up to 8 heavy atoms is able to generalize to molecules with up to 54 atoms [1]. Excitingly for my research, ANI-2 [2] can replace UFF or MMFF as the energy minimization step for conformation generation in RDKit [5].
So let’s use Auto3D [2] to generated low energy conformations for the four molecules caffeine, Ibuprofen, an experimental hybrid peptide, and Imatinib:
CN1C=NC2=C1C(=O)N(C(=O)N2C)C CFF CC(C)Cc1ccc(cc1)C(C)C(O)=O IBP Cc1ccccc1CNC(=O)[C@@H]2C(SCN2C(=O)[C@H]([C@H](Cc3ccccc3)NC(=O)c4cccc(c4C)O)O)(C)C JE2 Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CCN(CC5)C STIContinue reading
Recently I’ve been thinking a lot about how to decompose a compound into smaller fragments specifically for a retrosynthetic purpose. My question is: given a compound, can I return building blocks that are likely to synthesize together to produce this compound simply by breaking likely bonds formed in a reaction? A method that is nearly 15 years old named, breaking of retrosynthetically interesting chemical substructures (BRICS), is one approach to do this. Here I’ll explore how BRICS can reflect synthetic accessibility.
Continue readingWhen recently looking at some reaction data, I was confronted with the problem of atom-to-atom mapping (AAM) and what tools are available to tackle it. AAM refers to the process of mapping individual atoms in reactants to their corresponding atoms in the products, which is important for defining a reaction template and identifying which bonds are being formed and broken. This has many downstream uses for computational chemists, such as for reaction searching and forward and retrosynthesis planning1. The problem is that many reaction databases do not contain these mappings, and annotation by expert chemists is impractical for databases containing thousands (or more) data points.
Continue readingToday’s post builds on my earlier blogpost on how to turn a SMILES string into an extended-connectivity fingerprint using RDKit and describes an interesting and easily implementable modification of the extended-connectivity fingerprint (ECFP) featurisation. This modification is based on representing the atoms in the input compound at a different (and potentially more useful) level of abstraction.
We remember that each binary component of an ECFP indicates the presence or absence of a particular circular subgraph in the input compound. Circular subgraphs that are structurally isomorphic are further distinguished according to their inherited atom- and bond features, i.e. two structurally isomorphic circular subgraphs with distinct atom- or bond features correspond to different components of the ECFP. For chemical bonds, this distinction is made on the basis of simple bond types (single, double, triple, or aromatic). To distinguish atoms, standard ECFPs use seven features based on the Daylight atomic invariants [1]; but there is also another less commonly used and often overlooked version of the ECFP that uses pharmacophoric atom features instead [2]. Pharmacophoric atom features attempt to describe atomic properties that are critical for biological activity or binding to a target protein. These features try to capture the potential for important chemical interactions such as hydrogen bonding or ionic bonding. ECFPs that use pharmacophoric atom features instead of standard atom features are called functional-connectivity fingerprints (FCFPs). The exact sets of standard- vs. pharmacophoric atom features for ECFPs vs. FCFPs are listed in the table below.
In RDKit, ECFPs can be changed to FCFPs extremely easily by changing a single input argument. Below you can find a Python/RDKit implementation of a function that turns a SMILES string into an FCFP if use_features = True and into an ECFP if use_features = False.
# import packages
import numpy as np
from rdkit.Chem import AllChem
# define function that transforms a SMILES string into an FCFP if use_features = True and into an ECFP if use_features = False
def FCFP_from_smiles(smiles,
R = 2,
L = 2**10,
use_features = True,
use_chirality = False):
"""
Inputs:
- smiles ... SMILES string of input compound
- R ... maximum radius of circular substructures
- L ... fingerprint-length
- use_features ... if true then use pharmacophoric atom features, if false then use standard DAYLIGHT atom features
- use_chirality ... if true then append tetrahedral chirality flags to atom features
Outputs:
- np.array(feature_list) ... FCFP/ECFP with length L and maximum radius R
"""
molecule = AllChem.MolFromSmiles(smiles)
feature_list = AllChem.GetMorganFingerprintAsBitVect(molecule,
radius = R,
nBits = L,
useFeatures = use_features,
useChirality = use_chirality)
return np.array(feature_list)
The use of pharmacophoric atom features makes FCFPs more specific to molecular interactions that drive biological activity. In certain molecular machine-learning applications, replacing ECFPs with FCFPs can therefore lead to increased performance and decreased learning time, as important high-level atomic properties are presented to the learning algorithm from the start and do not need to be inferred statistically. However, the standard atom features used in ECFPs contain more detailed low-level information that could potentially still be relevant for the prediction task at hand and thus be utilised by the learning algorithm. It is often unclear from the outset whether FCFPs will provide a substantial advantage over ECFPs in a given application; however, given how easy it is to switch between the two, it is almost always worth trying out both options.
[1] Weininger, David, Arthur Weininger, and Joseph L. Weininger. “SMILES. 2. Algorithm for generation of unique SMILES notation.” Journal of Chemical Information and Computer Sciences 29.2 (1989): 97-101.
[2] Rogers, David, and Mathew Hahn. “Extended-connectivity fingerprints.” Journal of Chemical Information and Modeling 50.5 (2010): 742-754.
OPIG recently celebrated its 20th year; and on 10 January 2023 I gave a talk just a day before the 10th anniversary of BLOPIG’s first blog post. It’s worth reflecting on what’s stayed the same and what’s changed since then.
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