High-Dimensional Pharmacology

I have never done a cyclic peptide design in my life. So, when I was approached with a question on how to optimize the permeability of one, I guessed that more lipophilicity would help, and likely there are some specific neutral "vectors" that will also help. But the usual step is to go thru several papers and get the answer, obvious, right? I started with a very good article : "Optimizing PK properties of cyclic peptides: the effect of side chain substitutions on permeability and clearance". Read it, made conclusions and decided to feed it to ChatGPT (3.5) with request to "please extract very concise medicinal chemistry insights that can be further reused for peptide optimization". Here is what I got and and that was very neat: 1. Modification of peptide side chains significantly alters their log D values and subsequently their in vitro properties. For instance, aromatic or polar aliphatic side chains didn't significantly reduce log D, whereas ioniz...

This is a test of the pandas_ai by package by Gabriele Venturi . PandasAI is a layer between the pandas data frame and LLMs that simplifies interaction with the data and makes it more conversational.  I did a quick test for AstraZeneca clearance data from ChEMBL to test if pandas_ai will ease data manipulation.  Google colab notebook is here . **TLDR** : the proposed solution could be more optimal, but it's working! Some issues with linking abstract things, but current LLMs do not have a defined knowledge graph, so there are no surprises. More packages will appear soon, that will ease interaction with private data. The algorithm behind the package provides real code for data frame filtering and manipulation. Because of this, it will fail if the data types of columns are not correctly defined.  Simple request : Provide names for 5 compounds with the highest LogP. Answer : looks excellent, proper linking name to 'Molecule Name' and 'IDs' to 'Molecule ChEMBL ID...

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 ...