🍁

[Asia. @kiissedbypoem]

aku juga menantikan adaptasi anime dari light novel dan manga “re-living my life with a boyfriend who doesn't remember me”

nggak sabar melihat couple kesayanganku vincent dan oriana gerak dan bersuara. 🥹🩷

[Anime Posts @AnimeePost]

4am now vs 4am in 2020

[Ali Rashedi @AliRashediG]

نوروساینس واقعا جذابه :)
چرا باید هنوز از کاغذ و قلم استفاده کنیم؟

پژوهش‌های علوم اعصاب نشون می‌دن نوشتن با دست، شبکه‌های گسترده‌تری از مغز رو نسبت به تایپ‌کردن درگیر می‌کنه و ممکنه به یادگیری بهتر کمک کنه.

مقاله اصلی و خلاصه صوتیش رو گذاشتم توی کانال 👇
https://t.co/KxCCakfEB5

[Ben Birnbaum @benbirnbaum]

We’ve won our 4th consecutive OpenADMET Blind Challenge, coming ahead of 270 entrants, this time for predicting CYP direct inhibition.

Why do we keep winning? The short answer is our data, but it goes deeper than that.

Our goal at Inductive is to build superhuman chemical intelligence. In chemistry, the most valuable data remains orders of magnitude slower and more expensive to generate than in domains like coding and math, which have benefited from the near-infinite scalability of compute and reliable verification.

Because of this cost, any strategy for building chemical intelligence that relies on brute force data generation alone – even if you have billions of dollars to spend – will lose to a strategy that also aggregates data already being generated as part of economically valuable work.

That’s why, from the start, we’ve built products and services that generate immense value to drug discovery scientists while building a consortium to access large amounts of diverse, experimentally relevant chemistry data as a byproduct. That data enables us to build better models for the drug hunters we work with.

Doing this effectively requires trust from scientists who have decades of scar tissue dealing with the unending complexities of biology while being told again and again by Silicon Valley that technology is about to change everything. Earning that trust has taken years, and we take it seriously.

It’s also in the DNA of our team, where the best ML scientists and software engineers I know work with veteran drug discovery scientists. Together, we dream big while simultaneously thinking pragmatically about how to improve drug discovery today.

Thank you to OpenADMET for organizing yet another well-run competition and thank you to our customers for continuing to trust us to support their drug programs.

[Curious Minds @CuriousMindsHub]

Write by hand.
Write by hand.
Write by hand.
Write by hand.

Because your brain learns better that way.

[William L. Jorgensen @JorgensenWL]

Announcing LigParGen 3.0: OPLS-AA parameters for organic molecules, now a free app for Mac, Windows and Linux.

Paste a SMILES or draw a molecule, and get files for OpenMM, GROMACS, AMBER, CHARMM, LAMMPS and more.

https://t.co/aVTXJPcxI3

[🍁 @NASM_99]

Larhh tau2 dh kawen hahahaahah

[Kevin A. Scott, PhD @Sculpturatus]

credit to chris de savi

[Suman Chakrabarty @SumanPhysChem]

New paper out in @JCIM_JCTC! When a drug unbinds from a protein, it often takes multiple competing exit routes. Standard rare-event simulations give you a single global unbinding rate (k_off), but which pathway actually carries the reactive flux? Read here: https://t.co/ta5Bcgg41p

We introduce a Neural Network-Guided Weighted Ensemble workflow (in PathGennie v2.0) that turns qualitative pathway clustering into rigorous, pathway-resolved rate constants, without biasing the underlying MD dynamics: PathGennie sampling -> DTW clustering -> NN-refined PathCVs -> Pre-seeded WE.

Key insight on drug resistance in Abl kinase: Geometric pathway population != kinetic flux! In wild-type Abl, imatinib primarily exits via the P-loop channel. In the clinical N368S-resistant mutant, reactive flux flips dramatically to the alpha-C-helix route, driving faster drug escape.

Huge congrats to Dibyendu Maity @MAITYDIBYENDU99, Shaheerah Shahid & Sayari Bhattacharya @Sayari_2000! Big thanks to our collaborator, Prof. Rupak Majumdar! Grateful to @ANRFIndia for funding support and NSM Param Rudra for compute.

Code: https://t.co/bJxmLr05HN

[Kevin A. Scott, PhD @Sculpturatus]

students should be able to recognize the most common drug structures and guess at least some of the relevant information like drug class, indication, or target class. this is just pattern recognition. here are the most common chemotypes.

[🍁 @NASM_99]

Akhirnyaaaa, aku dh reti canne nk setup another screen ni 😂. Ingt kena guna app dia tpi linux tak support. Jumpaa jugak setting display 😬

[琪琪 @yukinosaurus_]

Weiii citer ni sumpah underrated

[Katsumi Tori 💓 @Nyan_Katsumi]

@liminelxo It's from Midnight Occult Civil Servants

[Mike White @genologos]

Wet lab biologists interested in building basic computing skills:

This excellent book is 70% off at Princeton University Press. Great intro to UNIX, R, Python, LaTex, etc. for those with no experience.

I use this in my Intro to Coding lectures.

https://t.co/8iCa5684Gh

[Obi 💫 @pluviioooophile]

Like I said before, I work best at night 🙂‍↕️

[Dhruv kumar @Dhruvkumar16797]

INSTEAD OF WATCHING NETFLIX TONIGHT. Spend 2 hours with this. Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything. The people who watch this tonight will wake up tomorrow with a new skill. Watch it and bookmark it now.

[Obi 💫 @pluviioooophile]

1st time after months balik keje langit clear gini alhamdulillah

[Obi 💫 @pluviioooophile]

I always have a soft spot for mcs with tough childhood but still able to retain their softness

jun has all my heart :')

[🫧 @lunecrumbss]

calm blue sea serenity 💙

[Obi 💫 @pluviioooophile]

Out celebrating @frhhnim_ 's birthday 🥳🎂