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. Here be dragons! (Not of any kind, but kaijū of the terrible sequel variety) When there is a single parent hit and it is a sing ..read more

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 ..read more

Have you ever needed to find a reaction SMARTS pattern for a certain reaction but don’t have it already written out? Do you have a reaction SMARTS pattern but need to test it on a set of reactants and products to make sure it transforms them correctly and doesn’t allow for odd reactants to work? I recently did and I spent some time developing functions that can: Generate a reaction SMARTS for a reaction given two reactants, a product, and a reaction name. Check the reaction SMARTS on a list of reactants and products that have the same reaction name. What is SMARTS? SMARTS stands for SMiles A ..read more

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 doe ..read more

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. Dissociation constant and Gibbs free energy The dissociation constant, KD, is the ratio of the dissociation rate, koff, over the assoc ..read more

At OPIG Group Meetings, it’s customary to give “Conference Feedback” whenever any of us has recently attended a conference. Typically, people highlight the most interesting talks—either to them or others in the group. Having just returned from the 6th RSC-BMCS / RSC-CICAG AI in Chemistry Symposium, it was my turn last week. But instead of the usual perspective—of an attendee—I spoke briefly about how to organize a conference. I’ve helped organize a few conferences over the years, notably the RSC AI in Chemistry symposium (as Co-Chair of the Organizing Committee for the last two years), and the ..read more

When designing machine learning algorithms with the aim of accelerating the discovery of novel and more effective therapeutics, we often care deeply about their ability to generalise to new regions of chemical space and accurately predict the properties of molecules that are structurally or functionally dissimilar to the ones we have already explored. To evaluate the performance of algorithms in such an out-of-distribution setting, it is essential that we are able to quantify the data shift that is induced by the train-test splits that we rely on to decide which model to deploy in production ..read more

We’ve been investigating deep learning-based protein-ligand docking methods which often claim to be able to generate ligand binding modes within 2Å RMSD of the experimental one. We found, however, this simple criterion can conceal a multitude of chemical and structural sins… DeepDock attempted to generate the ligand binding mode from PDB ID 1t9b (light blue carbons, left), but gave pretzeled rings instead (white carbons, right). If you’re interested in assessing the structural quality and chemical validity of predicted binding modes (and conformations) of small molecules, you might like to ..read more

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. Recognisable compounds The point of a placeholder molecule is an example whose identity does not matter. As a result a common practice is to choose a ..read more

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. Aim First, there are several resources on the basics of MCS, such as Greg Landrum’s tutorial on the subject. Herein, I want to discuss a specific use case: full control over the atom matching itself. There are tw ..read more