Channel
@chem_ml
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
826subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002420166750 |
|---|---|
| Type | Channel |
| Username | @chem_ml |
| Description | Daily articles and news from the field of machine learning in chemistry from the researchers of IGIC RAS @chemrussia For contact: @levkrasnov @st613laboratory @StasBezzubov |
| Created | Between 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/chem_ml |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 01:31 | 826 | no change |
| 7 Aug 2026, 14:36 | 826 | first reading |
17 posts held, back to 25 June 2026 — 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 pageof Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 7 August 2026 |
|---|---|
| Posts held | 17 (25 June 2026 – 7 August 2026) |
| Views total | 2,748 |
| Reactions total | 158 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 01:31 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Lifetime counters from Telegram’s own channel header, read 8 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
313 reactions across 17 posts, in 6 distinct kinds. The most used accounts for 35.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 111 | 35.5% | |
| 🔥 | 111 | 35.5% | |
| 👍 | 87 | 27.8% | |
| 😁 | 2 | 0.639% | |
| ❤🔥 | 1 | 0.319% | |
| 🎃 | 1 | 0.319% |
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 17 of the 17 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 313reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 17 most recent posts we hold, published 25 June 2026 to 7 August 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.
Benchmarking and developing large language models using one million clinical trials 🔥 https://www.nature.com/articles/s41746-026-02933-7 Here, we introduce TrialPanorama, a large-scale structured resource aggregating 1.6M clinical trial records from fifteen global registries linked with biomedical ontologies and literature. Using this resource, we construct 152K training and testing samples spanning eight clinical …
❤4👍3🔥3
Posted without readable text
❤13👍8🔥7❤🔥1
The past, present and future of self-driving laboratories https://www.nature.com/articles/s41570-026-00847-2 This Review traces the evolution of self-driving laboratories and examines the structural asymmetries that limit their maturation into shared scientific infrastructure. We frame the next phase of the field around three interdependent requirements: scalability, generalizability and provenance-complete experim…
❤3👍2🔥2
Posted without readable text
🔥24❤8👍8🎃1
Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints🔥 https://arxiv.org/abs/2607.18144 In this work, we systematically analyze whether current general-purpose LLMs are capable of navigating complex 3D constraints compared to established baselines such as specialized diffusion models. We consider 3D ligand generation conditioned on protein pockets together with ligand- and inte…
❤6👍4🔥4
Yield Smarter, Not Harder: Good Practices for Machine Learning of Reaction Outcomes https://doi.org/10.1021/jacs.6c02213 Reaction yield prediction is a longstanding challenge in synthetic chemistry, with broad implications for route planning, scalability, and high-throughput experimentation (HTE). While recent machine learning (ML) approaches have demonstrated promise in modeling reactivity, they often use complex …
👍5🔥4❤2
Data-Driven Insights into Ionic Conductivity in High-Dimensional Sodium Battery Electrolytes https://doi.org/10.1021/acsenergylett.6c01023 The discovery of advanced battery electrolytes is challenged by the vast compositional space of multi-component liquid formulations. Here, we introduce the ELectrolyte Laboratory for Integrated Experimentation (ELLIE), an automated platform that combines electrolyte formulation …
👍5❤3🔥3
https://chemrxiv.org/doi/10.26434/chemrxiv.15006080/v1 Коллеги из 🏛ИОНХ РАН, 🏛ИНЭОС РАН и Университета Барселоны выложили препринт про BLIND (Bimodal Learning from Imperfect NMR Data) — трансформер, который переводит спектры ¹H и/или ¹³C ЯМР напрямую в молекулярную структуру (SMILES). Что делает работу интересной: Модель не получает ни брутто-формулу, ни набор возможных фрагментов, ни элементный состав — только то…
❤17🔥12👍6
Strategies for Identifying Molecules of Interest in Large Chemical Spaces 🔥 https://pubs.acs.org/doi/10.1021/acs.jcim.6c01496 Searching in ultralarge Chemical Spaces with known 2D similarity metrics, like fingerprint-based Tanimoto, substructure, or pharmacophore similarity searches, contains pitfalls due to the representation of molecules as synthons with connectivity rules. Applied to a set of almost 3000 drug-r…
👍4❤3🔥2
🎊 Наш LLM-бенчмарк по растворимости SoluBench сегодня доехал до 📕 JCIM: https://pubs.acs.org/doi/10.1021/acs.jcim.6c00677 Это первый раз в нашей практике, когда за время рецензии в журнале результаты успели устареть минимум дважды — так как на момент сабмита у нас самая свежая модель была Opus 4.6, а сейчас уже во всю работают 4.8 и Fable :)) А еще у нас с JCIM'ом закончилась целая сага с серией desk rejection по п…
❤20🔥11👍8
In Silico ADMET: From Current Practices to Novel Profilers https://pubs.acs.org/doi/10.1021/acs.jmedchem.6c00049 We introduce OneADMET, a meticulously curated data set of 738,161 compounds with 1,119,719 measurements spanning 44 ADMET end points and 1 489 biological activities. We report a unified ChemProp-based MTL model capable of handling hundreds of continuous tasks simultaneously, which has practical advantage…
👍4❤3🔥3
Фантастика, конечно. Если бы мне кто-то в 2020 году сказал, что в 2026 можно будет одним запросом "Дай мне SMILES", получать SMILES всех комплексов с такой картинки (и без ошибок) с помощью модели, которую на это даже не файнтюнили — я бы не поверил. Про целесообразность, правда, отдельный вопрос. Потому что Opus 4.8 в low-режиме съел 6% пятичасового лимита токенов на это :)
🔥17👍4😁2❤1
Showing the 12 most recent of 17 posts we hold for @chem_ml. 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.
Citation-graph rank — 447,732 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
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 8 August 2026 — this entry's latest reading, not the date you are reading this.
“Chem ML/AI/Datasets” (@chem_ml), 826 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/chem_ml.
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.