Whereas it is easy to say in a paper “Given the HT-Sequential-ITC results, 42 led to 113, a substituted decahydro-2,6-methanocyclopropa[f]indene”, it is frequently rather trickier algorithmically figure out which atoms map to which. In Fragmenstein, for the placement route, for example, a lot goes on behind the scenes, yet for some cases human provided mapping may be required. Here I discuss how to get the mapping from Fragmenstein and what goes on behind the scenes.
I attended RSC Fragments 2024 (Hinxton, 4–5 March 2024), a conference dedicated to fragment-based drug discovery. The various talks were really good, because they gave overviews of projects involving teams across long stretches of time. As a result there were no slides discussing wet lab protocol optimisations and not a single Western blot was seen. The focus was primarily either illustrating a discovery platform or recounting a declassified campaign. The latter were interesting, although I’d admit I wish there had been more talk of organic chemistry —there was not a single moan/gloat about a yield. This top-down focus was nice as topics kept overlapping, namely:
On the eastern side of Oxfordshire are the Cotswolds, a pleasant hill range with a curious etymology: the hills of the goddess Cuda (maybe, see footnote). Cuda is a powerful yet wrathful goddess, and to be in her good side it does feel like druidry. The first druidic test is getting software to work: the wild magic makes the rules of this test change continually. Therefore, I am writing a summary of what works as of Late 2023.
Fragmenstein is a Python module that combine hits or position a derivative following given templates by being very strict in obeying them. This is done by creating a “monster”, a compound that has the atomic positions of the templates, which then reanimated by very strict energy minimisation. This is done in two steps, first in RDKit with an extracted frozen neighbourhood and then in PyRosetta within a flexible protein. The mapping for both combinations and placements are complicated, but I will focus here on a particular step the minimisation, primarily in answer to an enquiry, namely how does the RDKit minimisation work.
Chemoinformatics uses a curious jumble of terms from thermodynamics, wet-lab techniques and statistical terminology, which is at its most jarring, it could be argued, in machine learning. In some datasets one often sees pIC50, pEC50, pKi and pKD, in discussion sections a medchemist may talk casually of entropy, whereas in the world of molecular mechanics everything is internal energy. Herein I hope to address some common misconceptions and unify these concepts.
Secure shell (SSH) is an essential tool for remote operations. However, not everything with it is smooth-sailing. Especially, when you want to do things like reverse–port-forwarding via a proxy-hump or two a Jupyter notebook to your local machine from a compute node on a no-home container . Even if it sounds less plausible than the exploits on Mr Robot, it actually can work and requires zero social-engineering or sneaking in server rooms to install Raspberry Pis while using a baseball cap as a disguise.
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.
16:30 BST 27/06/2023 Oxford, UK. A large number of scientists were spotting riding bicycles across town, to the consternation of onlookers. The event was the Oxford Protein Informatics Group (OPIG) “tour de farce” 2023. A circular bike ride from the Department of Statistics, to The Up in Arms (Marston), The Trout Inn (Godstow), The Perch (Port Meadow) and The Holly Bush (Osney Island). This spurred great bystander-anxiety due to one of a multitude of factors: the impressive size of the jovial horde, the erraticism of the cycling, the deplorable maintenance of certain bikes, and the unchained bizarrerie of the overheard dialogue.
Finding 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.
Environment modules is a great tool for high-performance computing as it is a modular system to quickly and painlessly enable preset configurations of environment variables, for example a user may be provided with modulefile for an antiquated version of a tool and a bleeding-edge alpha version of that same tool and they can easily load whichever they wish. In many clusters the modules are created with a tool called EasyBuild, which delivered an out-of-the-box installation. This works for things like a single binary, but for conda this severely falls short as there are many many configuration changes needed.
