BenCree

Postdoc @RIKEN, PhD from @ColegroupNCL Newcastle University. Computational Chemist/Drug Design & Open Source Dev. Fan of SC2. An English flavoured scientist.

[BenCree @ChemCree]

@willyiamm Ah ok got you now

[BenCree @ChemCree]

@willyiamm Molecular sieves not good enough? What's the relative efficacy of the two methods?

[BenCree @ChemCree]

@MaskedTorah @ptrschmdtnlsn @s7nfo I dont think a single molecule of ethanol assures a theoretical (whatever that means here) possibility Liquids are in dynamic equilibrium with their gaseous phase, so youd have to somehow select an arbitrary molecule. At this scale thermodynamic properties completely break down

[BenCree @ChemCree]

@ptrschmdtnlsn @s7nfo @MaskedTorah As you say, collecting the enriched liquid fraction is just uneconomical though!

[BenCree @ChemCree]

@ptrschmdtnlsn @s7nfo @MaskedTorah I think it's possible for that particular phase diagram. The water-ethanol one converges at higher fractions of ethanol so it definitely is impossible though in terms of *just* boiling (not adding anything), at some point the vapour will always be identical to the liquid

[BenCree @ChemCree]

@ptrschmdtnlsn @s7nfo @MaskedTorah This sort of thing, where repeated boiling and condensing converges to a singular X:Y ratio, from either direction.

[BenCree @ChemCree]

@ptrschmdtnlsn @s7nfo @MaskedTorah The water vapour enrichment for large propanol fractions is ideal behaviour, adding water and boiling will slowly return you to the Azeotrope but from the other direction (reducing the proportion of propanol, not water, this time)

[BenCree @ChemCree]

@ptrschmdtnlsn @s7nfo @MaskedTorah In ideal cases it's exactly equal, and you get the Gibbs-Duhem equations (https://t.co/64XEWw2Za2).

For an Azeotrope the chemical potential of the gas and liquid phases are the same. Depending on solvent interactions you might get some complicated non-ideal behaviour, though.

[Gabriele Corso @GabriCorso]

📢 Call for proposals: Boltz small-molecule design collaboration! 🧬
Can we help design your ideal molecule? Can you help us improve our open-source models? Please reach out or share with scientists you know! More details below!

It has been great to see the level of excitement around our Boltz-2 release. Along with the structure and affinity model, we are working on new workflows for hit-discovery and hit-to-lead of small molecules. We have seen very strong computational results and we are now interested in validating them across a range of different targets and real-world settings.

Do you have a target on which you would like to design or optimize small-molecule binders? Do you have an associated chemical or functional assay that you can run to validate the designs? If the answer is yes to both questions, we would love to hear from you and collaborate.

As part of these collaborations, we would work alongside you to tailor our workflows for your task, run the large scale design on our cluster, and return to you the designed molecules alongside their expected scores. We ask you to commit to testing the top (according to your judgement) designed compounds and send us back the experimental results within 2 months of the selection.

You’ll have full IP of the compounds we send you and have the opportunity (not necessity) to be included alongside with the experiments you run in a joint journal publication. If this is something that might interest you, please reach out to me at gcorso at https://t.co/VkKWACM16p or DM me on the boltz-community Slack!

[Peyman Milanfar @docmilanfar]

A surprising & little-known results in classical statistics:

Mean (μ) and median (m) are within one std deviation:

|μ−m| ≤ σ

For unimodal densities, bound is even tighter

|μ−m| ≤ 0.7746 σ

This beautiful results first appeared in a 1932 paper by Hotelling & Solomons

1/3

[John D. Cook @JohnDCook]

You can estimate logarithms base 10 without a calculator with the approximation (x - 1)/(x + 1).

[Jorge Bravo Abad @bravo_abad]

Transforming molecular machine learning with stereoelectronics-infused molecular graphs (SIMGs)

Traditional molecular representations in machine learning often overlook essential quantum-mechanical effects, limiting predictive power in complex chemical systems. In my latest Substack post, I discuss recent work by Boiko, Reschützegger, Sánchez-Lengeling, Blau, and Gomes, who propose SIMGs—a novel molecular graph approach explicitly embedding quantum-chemical stereoelectronic details.

Their method leverages Natural Bond Orbital (NBO) analysis to significantly boost predictive accuracy, outperforming classical models on diverse molecular properties and even large biological systems previously inaccessible to quantum-level precision. This innovation not only advances molecular predictions but enhances interpretability and insight into subtle quantum interactions.

Link to your Substack post: https://t.co/YgioabwM9Q

[Josh Rackers @JoshRackers]

A new flagship quantum chemistry dataset is out! Omol25 contains more than 100 million DFT calculations across an expanse of chemical space.

Collaborating with the FAIR team on this has been super fun!

https://t.co/kPJHqPdvwH

[BenCree @ChemCree]

@KingsMaccChem Still a favourite of mine!

[BenCree @ChemCree]

@rafal_szabla N.b. I used this web interface: https://t.co/vjb159ANQN

[BenCree @ChemCree]

@rafal_szabla Here's what I used the last time I had to do one, simple text -> chart:

https://t.co/irGB3AVdMR

[Google DeepMind @GoogleDeepMind]

Today in @Nature, we’re presenting GenCast: our new AI weather model which gives us the probabilities of different weather conditions up to 15 days ahead with state-of-the-art accuracy. ☁️⚡

Here’s how the technology works. 🧵https://t.co/PWCNWbQnlU

[himanshu @himanshustwts]

this is lit. so satisfying to see conv nets in action man!