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 STI
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.
When 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.
Today’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.
The Astex Diverse set [1] is a dataset containing the crystallized poses of 85 protein-ligand complexes. It was introduced in 2007 to address problems in previous datasets such as incorrect ligand representation.
Loading the 85 ligand files with today’s version of the cheminformatics toolkit RDKit [2] is, however, not as straightforward as you might expect.
ECFPs were originally described in a 2010 article of Rogers and Hahn [1] and still belong to the most popular and efficient methods to turn a molecule into an informative vectorial representation for downstream machine learning tasks. The ECFP-algorithm is dependent on two predefined hyperparameters: the fingerprint-length L and the maximum radius R. An ECFP of length L takes the form of an L-dimensional bitvector containing only 0s and 1s. Each component of an ECFP indicates the presence or absence of a particular circular substructure in the input compound. Each circular substructure has a center atom and a radius that determines its size. The hyperparameter R defines the maximum radius of any circular substructure whose presence or absence is indicated in the ECFP. Circular substructures for a central nitrogen atom in an example compound are depicted in the image below.
Drawing molecules on your laptop usually requires access to proprietary software such as ChemDraw (link) or free websites such as PubChem’s online sketcher (link). However, if you are feeling adventurous, you can build your personal sketcher in React/Typescript using the Ketcher package!
Ketcher is an open-source package that allows easy implementation of a molecule sketcher into a web application. Unfortunately, it does require TypeScript so the script to run it cannot be imported directly into an HTML page. Therefore we will set up a simple React app to get it working.
The sketcher is very sleek and has a vast array of functionality, such as choosing any atom from the periodic table and being able to directly import molecules from either SMILES or Mol2/SDF file format into the sketcher. These molecules can then be edited and saved to a new file in the chemical file format of your choosing.
This is my first OPIG blog! I’m going to start with a summary of the Graphormer, a Graph Neural Network (GNN) that borrows concepts from Transformers to boost performance on graph tasks. This post is largely based on the NeurIPS paper Do Transformers Really Perform Bad for Graph Representation?by Ying et. al., which introduces the Graphormer, and which we read for our last deep learning journal club. The project has now been integrated as a Microsoft Research project.
I’ll start with a cheap and cheerful summary of Transformers and GNNs before diving into the changes in the Graphormer. Enjoy!
Graph neural networks (GNNs) have quickly become one of the most important tools in computational chemistry and molecular machine learning. GNNs are a type of deep learning architecture designed for the adaptive extraction of vectorial features directly from graph-shaped input data, such as low-level molecular graphs. The feature-extraction mechanism of most modern GNNs can be decomposed into two phases:
Message-passing: In this phase the node feature vectors of the graph are iteratively updated following a trainable local neighbourhood-aggregation scheme often referred to as message-passing. Each iteration delivers a set of updated node feature vectors which is then imagined to form a new “layer” on top of all the previous sets of node feature vectors.
Global graph pooling: After a sufficient number of layers has been computed, the updated node feature vectors are used to generate a single vectorial representation of the entire graph. This step is known as global graph readout or global graph pooling. Usually only the top layer (i.e. the final set of updated node feature vectors) is used for global graph pooling, but variations of this are possible that involve all computed graph layers and even the set of initial node feature vectors. Commonly employed global graph pooling strategies include taking the sum or the average of the node features in the top graph layer.
While a lot of research attention has been focused on designing novel and more powerful message-passing schemes for GNNs, the global graph pooling step has often been treated with relative neglect. As mentioned in my previous post on the issues of GNNs, I believe this to be problematic. Naive global pooling methods (such as simply summing up all final node feature vectors) can potentially form dangerous information bottlenecks within the neural graph learning pipeline. In the worst case, such information bottlenecks pose the risk of largely cancelling out the information signal delivered by the message-passing step, no matter how sophisticated the message-passing scheme.
