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...

Kaggle Solubility competition is over. It did draw the attention of 100 participants with some strong issues underlined by the participants. I decided to share my dive into this... Data: 70 711 compounds in train set: A very good and diverse dataset.   30 308 compounds in test set . Quality m etric: Kappa coefficient  (was it changed later?). Results Initial winner (by leaderboard) was a Ohue laboratory of Tokyo Institute of Technology with a combination of the BART transformer language model and GNN with focal loss  ( solution ).  The dramatic difference between the winner and the rest of the participants was noticeably big and raised a lot of questions. Bernhard Rohde (Novartis) was the second leaderboard winner. He underlined the same issue and showed that “plate/row/column” has a drastic effect on model quality (explicit IDs were provided: EOS12286). Still, the Bayesian Instance-Based Offset Learning solution he used draw my attention, because it can be app...