High-Dimensional Pharmacology

 T his is a first of the “lets-test-this” posts: Google Colab notebook is here . TLDR : Jazzy features does not have positive impact on solubility prediction. Intro: hydrogen bonds drive all kind of interactions, and no doubt are important for drug design process. A nice package from AstraZeneca was published recently that predict of hydrogen-bond strengths and free energies of hydration of small molecules. It looks like that the main application of Jazzy is for local settings – protein-ligand strength estimation, SAR studies, but my interest was to test it on compound solubility prediction. Goal : testing if free energy of hydration (without free parameters) from Jazzy has an impact on prediction of compound solubility. Setup: Data was taken from SolCuration, that's a derivate from Meng et al paper (AqSol) + additional curated datasets. In case of duplicated measurements, a median was taken and compounds with SD > 0.5 were removed (~1500 drop). Three types of descriptor...

  Generating 3D structure of the compounds from prior knowledge is a challenge. Machine learning models can be used to accurately predict 3D conformers of small molecules and larger compounds (e.g. AlphaFold). GeoMol , TorsionNet and alike methods are basically taking well-known knowledge-based methods for 3D generation on the next level.   But none of the published models can be reproduced without any pretraining and/or easy to install, except for Auto3D . Auto3D was developed in the group of Professor  Olexandr Isayev  and published in  2022 including code . Auto3D automatically generates conformational space further optimized by atomistic neural network potentials (NNPs) such as ANI or AIMNet (also by the Isayev Lab). Tautomers and isomer enumeration are in my opinion is not necessary as part of the package, but they are nice addition.   I tested for 10 random drug-like compounds from ChEMBL. For whatever reason running algorithm on CPU gave me abys...

The text below is a private opinion not linked in any form to my current employee.   So, all hands on deck… the AI revolution everyone was talking about for a long time arrived. I played a bit with ChatGPT for some of the general R&D and chemoinformatics tasks… and saying that impressed is nothing, I am shocked by the quality of the generated text and code, and the future possibilities of those products. Some people claim that ChatGPT will not replace search engines. With a bit of time, it will likely be a merge between personal assistance and worldwide web search engines. ChatGPT and its successors will revolutionize how software is built, increasing productivity while reducing costs. That, in turn, gives programmers more flexibility and time for more complex tasks. Furthermore, these tools can help bridge gaps between different programming languages, making it easier for coders to operate in heterogeneous environments. ChatGPT-like models will overtake enterprise text mining ...

  It's been a while, but I found a very interesting paper that I want to comment. Proteomics is an invaluable tool for unlocking the secrets of compound polypharmacology: by quantifying the protein expression changes caused by different compounds, researchers can gain valuable insight into compound interactions at the cellular level. Proteomics in application to the understanding of compound polypharmacology is relatively well established : there are tons and tons of publications on this topic about single compounds or some SAR exploration with 10s of compounds max… and now this goldmine was published – “ A proteome-wide atlas of drug mechanism of action ”. 875 compounds, 875 proteomics profile after 24h treatment (in duplicate) on ~9000 proteins in HCT116 cell line . To be honest, nothing radically new was done in this paper, but the robustness, richness, and accessibility of data that were generated in this paper are, in my opinion, unprecedented. Articles like this have a globa...