Virtual screening is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures that bind tightly and specifically to a given protein target. Many machine learning (ML) models have been proposed for virtual screening, however, it is not clear whether these models can truly predict the molecular properties accurately across chemical space or simply overfit the training data. As chemical space contains clusters of molecules around scaffolds, memorising the properties of a few scaffolds can be sufficient to perform well, masking the fact that the model may not generalise beyond close analogue. Different debiasing algorithms have been introduced to address this problem. These algorithms systematically partition the data to reduce bias and provide a more accurate metric of the model performance.
Continue readingCategory Archives: Cheminformatics
AutoDock 4 and AutoDock Vina
A recently just-released publication from Ngyuen et al. ing JCIM pointed out that while AutoDock Vina is faster, AutoDock 4 tends to have better correlation with experimental binding affinity.1
[This post has been edited to provide more information about the cited paper, as well as providing additional citations.]
Ngyuyen et al. selected 800 protein-ligand complexes for 47 protein targets that had both experimental PDB structures complexed with a ligand, as well as their associated binding affinity values.
Continue readingJournal Club: Is our data biased, and should it be?
Last week I presented the above paper at group meeting. While a little different from a typical OPIG journal club paper, the data we have access to almost certainly suffers from the same range of (possible) biases explored in this paper.
Continue readingNeurIPS 2019: Chemistry/Biology papers
NeurIPS is the largest machine learning conference (by number of participants), with over 8,000 in 2017. This year, the conference will be held in Vancouver, Canada from 8th-14th December.
Recently, the list of accepted papers was announced, with 1430 papers accepted. Here, I will highlight several of potential interest to the chem-/bio-informatics communities. Given the large number of papers, these were selected either by “accident” (i.e. I stumbled across them in one way or another) or through a basic search (e.g. Ctrl+f “molecule”).
Continue readingWhen OPIGlets leave the office
Hi everyone,
My blogpost this time around is a list of conferences popular with OPIGlets. You are highly likely to see at least one of us attending or presenting at these meetings! I’ve tried to make it as exhaustive as possible (with thanks to Fergus Imrie!), listing conferences in upcoming chronological order.
(Most descriptions are slightly modified snippets taken from the official websites.)
BOKEI: Bayesian Optimization Using Knowledge of Correlated Torsions and Expected Improvement for Conformer Generation
In previous blog post, we introduced the idea of Bayesian optimization and its application in finding the lowest energy conformation of given molecule[1]. Here, we extend this approach to incorporate the knowledge of correlated torsion and accelerate the search.
Continue readingTrying out some code from the Eighth Joint Sheffield Conference on Chemoinformatics: finding the most common functional groups present in the DSPL library
Last month a bunch of us attended the Sheffield Chemoinformatics Conference. We heard many great presentations and there were many invitations to check out one’s GitHub page. I decided now is the perfect time to try out some code that was shown by one presenter.
Peter Ertl from Novartis presented his work on the The encyclopedia of functional groups. He presented a method that automatically detects functional groups, without the use of a pre-defined list (which is what most other methods use for detecting functional groups). His method involves recursive searching through the molecule to identify groups of atoms that meet certain criteria. He used his method to answer questions such as: how many functional groups are there and what are the most common functional groups found in common synthetic molecules versus bioactive molecules versus natural products. Since I, like many others in the group, are interested in fragment libraries (possibly due to a supervisor in common), I thought I could try it out on one of these.
Continue readingWhich fragment first?
Crystallographic fragment based lead discovery is a now a routine technique, which can sample 1000’s of compounds per week. But how do we identify the most appropriate compounds to screen against our target of interest?
Continue readingConstrained Embedding with RDKit
This blog post explores the RDKit function ConstrainedEmbed.
Continue readingGraph-based Methods for Cheminformatics
In cheminformatics, there are many possible ways to encode chemical data represented by small molecules and proteins, such as SMILES, fingerprints, chemical descriptors etc. Recently, utilising graph-based methods for machine learning have become more prominent. In this post, we will explore why representing molecules as graphs is a natural and suitable encoding. Continue reading