Category Archives: Cheminformatics
NeurIPS 2020: Chemistry / Biology papers
Another blog post, another look at accepted papers for a major ML conference. NeurIPS joins the other major machine learning conferences (and others) in moving virtual this year, running from 6th – 12th December 2020. In a continuation of past posts (ICML 2020, NeurIPS 2019), I will highlight several of potential interest to the chem-/bio-informatics communities
The list of accepted papers can be found here, with 1,903 papers accepted out of 9,467 submissions (20% acceptance rate).
In addition to the main conference, there are several workshops highly related to the type of research undertaken in OPIG: Machine Learning in Structural Biology and Machine Learning for Molecules.
The usual caveat: 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”). If you find any I have missed, please reach out and I will update accordingly.
Continue readingUnderstanding Conformational Entropy in Small Molecules
While entropy is a major driving force in many chemical changes and is a key component of the free energy of a molecule, it can be challenging to calculate with standard quantum thermochemical methods. With proper consideration in flexible molecules, we can break down the total entropy into different components, including vibrational, translational, rotational and conformational entropy. The calculation of conformational entropy is the most time-consuming as we have to sample all thermally-accessible conformers. Here, we attempt to understand the components that contribute to the conformational entropy of a molecule, and develop a physically-motivated statistical model to rapidly predict the conformational entropies of small molecules.
Continue readingICML 2020: Chemistry / Biology papers
ICML is one of the largest machine learning conferences and, like many other conferences this year, is running virtually from 12th – 18th July.
The list of accepted papers can be found here, with 1,088 papers accepted out of 4,990 submissions (22% acceptance rate). Similar to my post on NeurIPS 2019 papers, I will highlight several of potential interest to the chem-/bio-informatics communities. As before, 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 readingUnderstanding the synthesizability of molecules proposed by generative models
De novo molecular design is a computational technique to generate molecules with desired properties from scratch. Classical generative algorithms are based on Genetic Algorithms (GA) and the iterative construction of molecules from molecular fragments. Recently, Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs) have been developed for this task, however, the synthesizability of the proposed molecular structures remains an issue. Gao and Coley[1] provided an analysis of the synthesizability of the molecules proposed by these de novo generative algorithms, and discuss their strengths and weaknesses.
Continue readingDeLinker – Deep Generative Models for 3D Linker Design
*** Disclaimer: This blog post represents some shameless self-promotion. ***
I am delighted to announce that our most recent work, DeLinker, was recently published in the Journal of Chemical Information and Modeling (link).
CCK-18 is Going Virtual
We are going virtual! Our next Comp Chem Kitchen, CCK-18, will be via a Zoom Webinar, on Friday, March 27, 2020, at 5-6 pm. We are delighted to announce that Prof. Andreas Bender from the University of Cambridgewill be speaking, as well as Dr Vicky Hellon from F1000 Research. To attend the CCK-18 webinar, you must sign up for a free Eventbrite ticket (limit 100).
Visualisation of very large high-dimensional data sets as minimum spanning trees
Large high-dimensional data sets are frequently used in chemical and biological sciences. For example the ChEMBL database contain millions of bioactive molecules from the scientific literature and their associated biological assay data are usually used for drug discovery. Visualising such databases helps understand the structure of data.
Continue readingBayesian Optimization and Correlated Torsion Angles—in Small Molecules
Our collaborator, Prof. Geoff Hutchison from the University of Pittsburg recently took part in the Royal Society of Chemistry’s 2020 Twitter Poster Conference, to highlight the great work carried out by one of my DPhil students, Lucian Leung Chan, on the application of Bayesian optimization to conformer generation:
State of the art in AI for drug discovery: more wet-lab please
The reception of ML approaches for the drug discovery pipeline, especially when focused on the hit to lead optimization process, has been rather skeptical by the medchem community. One of the main drivers for that is the way many ML publications benchmark their models: Historic datasets are split into two parts, with the larger part used to train and the smaller to test ML models. In order to standardize that validation process, computational chemists have constructed widely used benchmark datasets such as the DUD-E set, which is commonly used as a standard for protein-ligand binding classification tasks. Common criticism from medicinal chemists centers on the main problem associated with benchmark datasets: the absence of direct lab validation.
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