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:
Machine learning (ML) for biological/biomedical applications is very challenging – in large part due to limitations in publicly available data (something we recently published about [1]). Substantial amounts of time and resources may be required to generate the types of data (eg protein structures, protein-protein binding affinity, microscopy images, gene expression values) required to train ML models, however.
In cases where there is sufficient data available to provide signal, but not enough for the desired performance, ML strategies can be employed:
Language models have token the world by storm recently and, given the already explored analogies between protein primary sequence and text, there’s been a lot of interest in applying these models to protein sequences. Interest is not only coming from academia and the pharmaceutical industry, but also some very unlikely suspects such as ByteDance – yes the same ByteDance of TikTok fame. So if you also fancy trying your hand at building a protein language model then read on, it’s surprisingly easy.
Training your own protein language model from scratch is made remarkably easy by the HuggingFace Transformers library, which allows you to specify a model architecture, tokenise your training data, and train a model in only a few lines of code. Under the hood, the Transformers library uses PyTorch (or optionally Tensorflow) models, allowing you to dig deeper into customising training or model architecture, or simply leave it to the highly abstracted Transformers library to handle it all for you.
For this article, I’ll assume you already understand how language models work, and are now looking to implement one yourself, trained from scratch.
Last month, I had the privilege to attend the Keystone Symposium on Computational Design and Modeling of Biomolecules in beautiful Banff, Canada. This conference gave an incredible insight into the current state of the protein design field, as we are on the precipice of advances catalyzed by deep learning.
Being a jack of all trades brings to mind someone who has extensive multidisciplinary expertise and is equipped with many tools in their toolbox to solve different problems. A jack of all trades is a great succinct description for computational chemists in drug discovery.
Recently I had a great conversation with Dr. Arjun Narayanan, a Senior Research Scientist at Vertex Pharmaceuticals and a jack of all trades as a computational chemist. In this blog post, I’ll describe what he does as a computational chemist, the problems he solves, and the new tools he’s looking forward to adding to his toolbox.
During the first 6 months of my DPhil, I worked on clustering antibodies and I thought I would share what I learned about these algorithms. Clustering is an unsupervised data analysis technique that groups a data set into subsets of similar data points. The main uses of clustering are in exploratory data analysis to find hidden patterns or data compression, e.g. when data points in a cluster can be treated as a group. Clustering algorithms have many applications in computational biology, such as clustering antibodies by structural similarity. Actually, this is objectively the most important application and I don’t see why anyone would use it for anything else.
There are several types of clustering algorithms that offer different advantages.
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
Since its release, AlphaFold has been the buzz of the computational biology community. It seems that every group in the protein science field is trying to apply the model in their respective areas of research. Already we are seeing numerous papers attempting to adapt the model to specific niche domains across a broad range of life sciences. In this blog post I summarise a recent paper’s use of the technology for predicting protein-protein interfaces.
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 Rectified Linear Unit (ReLU) activation function was first used in 1975, but its use exploded when it was used by Nair & Hinton in their 2010 paper on Restricted Boltzmann Machines. ReLU and its derivative are fast to compute, and it has dominated deep neural networks for years. The main problem with the activation function is the so-called dead ReLU problem, where significant negative input to a neuron can cause its gradient to always be zero. To rectify this (har har), modified versions have proposed, including leaky ReLU, GeLU and SiLU, wherein the gradient for x < 0 is not always zero.
A 2020 paper by Naizat et al., which builds upon ideas set out in a 2014 Google Brain blog post seeks to explain why ReLU and its variants seem to be better in general for classification problems than sigmoidal functions such as tanh and sigmoid.
