Channel
@oprd_radar
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
75subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002671687943 |
|---|---|
| Type | Channel |
| Username | @oprd_radar |
| Description | Side channel of @epi_pharm |
| Created | Between 1 March 2025 and 31 July 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 9 August 2026 |
| Last confirmed live | 9 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/oprd_radar |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 9 Aug 2026, 21:15 | 75 | no change |
| 6 Aug 2026, 16:15 | 75 | first reading |
20 posts held, back to 30 August 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 pageof 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 20 posts for this entry, the most recent from 25 December 2025. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 9 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.
31 reactions across 20 posts, in 2 distinct kinds. The most used accounts for 90.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 28 | 90.3% | |
| ❤ | 3 | 9.68% |
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 20 of the 20 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 31reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 30 August 2025 to 25 December 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
#oprd There is always room to read about crystallisation development! This paper presented the general idea of using rapid process modeling as a parallel instrument to the widely applied DoE-based design and scale-up. Kind of overkill, but interesting to read for understanding how process could be described. https://pubs.acs.org/doi/10.1021/acs.oprd.4c00199
❤1👍1
#rnd It reminds me of another crowdsourced database collecting small-molecule synthesis routes. https://chemistrybydesign.oia.arizona.edu/
👍1
#rnd oligowiki is a curated, queryable database focused on therapeutic oligonucleotides and the chemistries that define them https://www.oligowizard.com/wiki/
👍1
#cmc #oprd Quality by digital design to accelerate sustainable medicines development If you get tired of QbD, now some people invent QbDD. Jokes aside - good review of what was already done and how QbD can evolve in digital era. https://doi.org/10.1016/j.ijpharm.2025.125625
👍1
Building ChemInformatic Agents with LangGraph A hands-on introduction to agents and tool calling - Build and customize AI agents that can reason, plan, and execute tasks in chemical research - Use tool calling to connect models with cheminformatics libraries - Explore real-world use cases like property prediction https://colab.research.google.com/drive/1nuuVA-1RTLqUC2AyKBc1OHmfPzdhhyJG
👍1
#rnd The preprint from Bayer applies WISP (Workflow for Interpretability Scoring using matched molecular Pairs) to real chemical datasets like LCAP yields, Factor Xa inhibition, and AMES mutagenicity. It shows how explainability methods can highlight which structural changes (e.g. adding a methyl group) influence model predictions. Importantly, WISP can also reveal when these explanations don’t reflect real chemistr…
👍1
#rnd #cmc This review summarizes key challenges and considerations in translating machine learning models into decision-making tools for real-world drug discovery projects, in particular, related to compound toxicity and safety. This includes making choices about data, modeling, validation, model metrics, and applying the model thus obtained to the process of drug discovery. https://pubs.acs.org/doi/10.1021/acs.che…
👍2
#rnd AI for Scientific Discovery is a Social Problem A tool is only as good as its user. https://arxiv.org/abs/2509.06580
👍2
#rnd Not exactly groundbreaking, but a smart and pragmatic shift from Iktos. Instead of asking “how do we make this designed molecule?” the authors flip the question to “what can we actually make from what’s already on the shelf - and what can we feed to the robots without endless reconfiguration?” Their cluster synthesis strategy groups diverse reactions into a few shared condition clusters, streamlining execution …
👍1
#oprd Cheeky robots are now coming for lab coats too, not just IT jobs. An interesting preprint introduces RAISE - a self-driving lab that fully automates formulation, contact-angle measurement, and optimization in a Bayesian closed loop. It turns surface science from tedious manual work into rapid, data-driven formulation discovery. Not pharma per se, but it could easily be applied there. https://arxiv.org/abs/251…
👍1
#cmc ICH is merging all stability guides into one big chunk - Q1A–E/Q5C will now live together as just Q1. What’s new: – Enhanced emphasis on knowledge- and risk-based approaches – New or expanded types of stability / supportive studies (f… finally) – Lifecycle / post-approval changes & commitments – New Annex for ATMPs and other new modalities A must-read for the grown-ups in pharma development - you’ll want to be…
👍1
#rnd The aforementioned SAscore and its successor BR-SAscore - still the best heuristic for synthetic accessibility, imho. Neither the modern SCscore, RAscore, nor SYBA manage to beat it. Very handy if you want to estimate which AI-slop molecules actually have a chance of being synthesised. https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8
👍1
Showing the 12 most recent of 20 posts we hold for @oprd_radar. 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 — 871,807 of 1,183,361entries 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.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
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 August 2026 — this entry's latest reading, not the date you are reading this.
“OPRD Radar” (@oprd_radar), 75 subscribers as measured 9 August 2026. Telegram Register, tgregister.com/channel/oprd_radar.
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.