Channel
@chemoinfo_papes
On this record: Growth · Engagement · Reactions · Posts · Telegram's recommendations · Cite this entry
998subscribers
+15 since we began measuring on 23 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001617431445 |
|---|---|
| Type | Channel |
| Username | @chemoinfo_papes |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 23 August 2026 |
| Last confirmed live | 9 September 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 9 September 2026 |
| On Telegram | t.me/chemoinfo_papes |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 9 Sept 2026, 12:20 | 998 | +9 |
| 31 Aug 2026, 03:23 | 989 | +6 |
| 24 Aug 2026, 07:36 | 983 | no change |
| 23 Aug 2026, 08:15 | 983 | first reading |
18 posts held, back to 22 June 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 18 posts for this entry, the most recent from 10 March 2026. An engagement rate over an empty window would be a number about nothing.
117 reactions across 13 posts, in 6 distinct kinds. The most used accounts for 33.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 39 | 33.3% | |
| ❤ | 35 | 29.9% | |
| ❤🔥 | 16 | 13.7% | |
| 🔥 | 16 | 13.7% | |
| ⚡ | 9 | 7.69% | |
| 💊 | 2 | 1.71% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 13 of the 18 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 117 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 18 most recent posts we hold, published 22 June 2025 to 10 March 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
If you're teaching chemoinformatics or drug design, this could be of interest to you. UCL published tutorial and Jupyther notebooks on docking using SMINA. Bare minimal, but it looks like important information is present, including presentation. https://github.com/UCL/Open_Docking_Lab_Handbook
⚡9👍6❤1
Signed Timur Madzhidov
Raymond lab decided to go further after GDB-17 and decided to collect GDB-20! The size is obviously too large 32 trillion structures, so they sampled subset by “GenerativeAI”. As to me, even their GDB-17 was a big crazy idea however it helped to understand how weird are randomly generated structures. But GDB-20… looks to me as an artefact of the epoch went for good… but great that this time they provide access to 12 …
👍3
Signed Timur Madzhidov
Wendi Warr shared her free report from the 2025 CINF Herman Skolnik Award symposia celebrating contribution of Professor Matthias Rarey. Quite interesting reading mostly about structure-based drug design. #SBDD https://drive.google.com/file/d/1aZcHqy07mSQKaq7My-R8WVjrXD1I6YPg/view
👍5
Signed Timur Madzhidov
Quite a nice and helpful open source tool - the Python code for finding pockets in proteins. Intsallable via pip and GitHub. #SBDD #bioinformatics #openscience #docking GitHub https://github.com/cch1999/pocketeer Article: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-168
🔥7❤5
Signed Timur Madzhidov
I think this tool could be rather useful for those who work in the drug design and especially structure based drug design. PocketMaster is a flexible and automated tool for analyzing, clustering, and visualizing protein binding sites. Main Functionalities of PocketMaster ✅ Automatic structure alignment ✅ Flexible methods for defining binding sites ✅ Support for multiple RMSD methods ✅ RMSD calculatio…
❤9👍2
Signed Timur Madzhidov
Grzybowski work shows how they used rather cheap robot for doing quite fancy study of reactivity and catalyst design. Rather interesting reading as to me but more from the point of view what could be done, and how insights can be gathered. Some code was open-sourced too, which look rather new for Grzybowski lab, good direction to go! #robochemistry #chemicalspace https://www.nature.com/articles/s41586-025-09490-1
👍2❤1🔥1
Signed Timur Madzhidov
MIT work on prediction of solubility in mixture of solvents. No rocket science or fancy ML as to me, just a well done work. But the model and data available. https://www.nature.com/articles/s41467-025-62717-7
❤🔥5❤2💊2👍1🔥1
Signed Timur Madzhidov
And adding to previous post. Kevin has just published (in September 2025, does he have time machine?) a paper on problems of testing LLMs. For the first time in my carrier I read it more like a scream from author's soul. Ok, there were articles like this on data reproducibility, but this one I take more personally, probably. #LLM https://www.sciencedirect.com/science/article/pii/S0927025625003842 BTW, it worth also…
❤2👍1
Signed Timur Madzhidov
Startup Harmonic develops AI chatbot for math reasoning with the idea to develop "mathematical superintelligence" (https://www.techticia.com/2025/07/harmonic-launches-aristotle-ai-chatbot.html). It is interesting when will we come to LLM that is on par with human in chemistry reasoning? So, far works of Philippe Schwaller and Kevin Jablonka show that LLM struggle in reasoning in chemistry domain. But it is amazing, t…
Signed Timur Madzhidov
Interesting benchmark of different neural network potentials (NNPs) to predict protein-ligand interaction energy. They used NNPs trained on materials-science data (Orb-v3 and MACE-MP-0b2-L), specific models for predicting certain biological targets (Orb-v3) and six NNPs trained on molecular data (ANI-2x, AIMNet2, Egret-1, eSEN-OMol25-sm-conserving, UMA-s, and UMA-m). For comparison, semi-empirical DFT (GFN2-xTB and g…
Signed Timur Madzhidov
Really cool tool was released by Rarey group: the list of 40 000 (!) functional group SMARTS and corresponding software that gives a list of groups that present in a molecule. The application is run as backend service, which I don't really like but can be helpful in some applications. But what is great - that SMARTS and their labels are available in csv file of the GitHub. That's supercool thing. Paper: https://pubs…
👍7❤4🔥3
Signed Timur Madzhidov
Amazing publication from Frank Noe and Microsoft Research team: generative model that predicts ensemble of peptide conformations. Basically, it is generative model that returns MD results at the costs of an hour. What a time we live in! Abstract: Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu,…
❤7👍4🔥3
Signed Timur Madzhidov
Showing the 12 most recent of 18 posts we hold for @chemoinfo_papes. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 9 September 2026 — this entry's latest reading, not the date you are reading this.
“Chemoinformatics papers” (@chemoinfo_papes), 998 subscribers as measured 9 September 2026. Telegram Register, tgregister.com/channel/chemoinfo_papes.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.