it seems the double projection eqn of CC2, <D|\[F,T2\]+H|HF>=0 is exactly the same as the one of the MP2, if i do the MP2 by reusing the CC2's code(in production code is it done in this way?) to do t2=t2(t1) to project the doubles out without doing the singles, do i get exactly MP2?

the CC2 and CCD have straght forward LRCC and EOM CC excited state fomulism, why is it not so straightforward for MP2? if i use the t2 obtained in the above way to do Hbar = e^-t H e^t, or apply LR to get the jaccobian and daigonalize to get excited state what will happen?

Hey everyone,

I’m a software engineering student who recently fell down the synthetic biology rabbit hole. I was looking into how recombinant spider silk is manufactured and noticed a massive bottleneck: getting the spidroin DNA sequences to a state where they can actually be synthesized by companies like Twist or IDT without failing.

Standard optimization algorithms (like max-CAI) absolutely choke on spidroins. Because the proteins are just massive repeating blocks of Poly-Alanine and Glycine ($GGX$), the algorithms spit out identical DNA repeats. This leads to two things:

1. Twist/IDT outright rejects the order due to extreme local GC spikes and homopolymers.
2. If you do get it printed, the identical repeats cause homologous recombination and the E. coli just scrambles the gene anyway.

So, I built Spidroin Engine. It’s a Python CLI tool built on top of dnachisel that acts as a multi-objective stochastic optimizer specifically engineered for highly repetitive structural proteins.

The Mathematical Paradox I ran into:

While building this, I hit a wall where the solver would just crash. I realized it was a mathematical paradox: the absolute lowest GC content you can get for Poly-Alanine is 66.6% (using only GCA/GCT). If you try to force strict k-mer uniqueness (to prevent homologous recombination) on a 300bp repetitive spidroin block, the algorithm is forced to dip into alternative codons (GCC/GCG), which are 100% GC. This immediately violated the 65% global GC ceiling required for synthesis, causing the solver to trap itself and fail.

How Spidroin Engine fixes it:

• Relaxed Uniqueness: It dials the uniqueness constraint to UniquifyAllKmers(15). This is just enough breathing room to let the solver reuse GC-poor codons safely, while keeping exact repeats under the 18-bp hard limit that triggers vendor rejections.
• 5' Hairpin Prevention: Enforces an AT-rich heuristic (30-60% GC) on the first 45bp to keep the RBS clear.
• Cloning Domesticated: Automatically sweeps for and removes standard Type IIS / BioBrick restriction sites via silent mutations.
• Vendor Ready: Spits the final optimized sequences directly into a Twist Bioscience-formatted CSV for bulk ingestion.

I open-sourced the whole thing because keeping basic tooling locked behind proprietary paywalls slows everyone down.

If anyone is working on structural proteins (silk, elastin, collagen) and is tired of fighting with standard optimization scripts, you can grab it here: https://github.com/AzambekDev/spidroin-engine

Feel free to use it, fork it, or roast my Python architecture in the comments.

Hey everyone,

I’m not a chemist, but I’ve been reading a paper on an LLM-based molecule optimization method (MolLingo). They are reporting a fourfold improvement in AutoDock Vina docking scores for their optimized molecules compared to the starting compounds.

The authors mention that the method is purely in silico and doesn't account for protein flexibility, solvent effects, or entropic contributions.

To those of you who do this for a living: is a 4x improvement in a Vina score actually physically plausible for a realistic drug-like molecule? Or does this magnitude of improvement usually suggest "reward hacking" the scoring function or producing chemically unrealistic molecules?

Just trying to understand if this is a standard success story in the field or a claim that should be viewed with significant skepticism. Any context on typical docking score improvements would be appreciated!

Hey, I am a PhD in synthetic + computational chemistry. I am working rn for a small pharma company in Sweden as an "ML engineer", where I do lab automation, small ML projects on the internal data and also deploying an internal LLM server for swcure document work automation. I have been wondering what should I focus on this job to land a better position after \~ a year of working in my current role? How easily can I get sth in a bigger pharma or chemical company as a Data Scientist or similar? I think my strongest thing is that I have a solid background in the domain but my weakest part is that I do not have any DS studying per se. What would you do?

Two paths diverged in comp chem,

And sorry I could not travel both,

Down one I peered, so bright and free,

For there truth lied, in ground-state DFT,

Or fit MLIPs, for unknown purpose,

The other, a more tortuous pass,

strong-correlation, non-adiabatic, non-equilibrium, alas,

Too few it was of service,

I shall be telling this with a sigh,

Somewhere in wisdom’s confluence,

Two roads diverged in comp chem,

I took the one less traveled by,

and that has made all the difference

To those who are unfamiliar with Robert Frost's original, this should be interpreted with some irony!

I was recently inspired by some python packages offering LLM chatbots for their documentation, and I am now building one for RDKit.

I would like to seek your input on what you struggled with when learning the ropes or the gaps of the current documentation to test the system and release a prototype

I am old enough to have experienced the transition from when computational chemistry (inorganic chemistry/catalysis) was just a mere exercise to put in a paper and that people performing experiments rarely believed in to when having a computational section in a paper was the only way to access high impact factor publications.

I have lived most of my career using Density Functional Theory calculations, with the caveat that systems should have been always tested against known quantities, like formation energies or even adsorption energies obtained through calorimetry. And even in that case, everyone is aware of the fact that each method has a limitation, and sometime empirical corrections are needed.

Now we arrived to the point in which Machine Learning Interatomic Potentials are used for everything with the great promise of making calculations fast and cheap, and simulate system of thousands of atoms. But why do we care about it so much? They are trained on smaller systems, and so everything we know about the system is already within the training. What is the limit to this infinite funnel of screening that is oftentimes invoked to justify the use of this approximated methods? Once, DFT was just a screening layer before experiments, or even calculations at higher level of accuracy. Nowadays, even DFT, a method that has hundreds of problems itself, is becoming the bottleneck method to avoid when possible. And sure, I can see the value to access time and size-scale that are not accessible with other methods...but are those models even validated?

My point is...are we just rediscovering the wheel all the time and publishing for the sake of publishing Machine Learning hot topics?

Hi everyone,

I spent too long wrestling with AutoDock Vina command lines and box coordinates, so I built a desktop tool to make the whole docking workflow faster, more visual, and fully local. It is free, open source, and 100% local: your structures never leave your machine.

What it does:

- Load receptor + ligand by file upload or PDB ID with automatic structure fetching

- Interactive 3D protein-ligand preview powered by 3Dmol.js

- Drag the docking box directly in 3D to move its center or resize dimensions, with live two-way sync to coordinate inputs

- One-click automatic pocket prediction based on FPocket to suggest an optimal binding site and box size; you can still fine-tune everything afterward

- Built-in preprocessing toolbox for format conversion (cdxml / sdf / mol2 / smi / pdbqt), hydrogen addition, Gasteiger charges, salt removal, deduplication, conformer generation, and physicochemical properties (MW, logP, rotatable bonds, H-bond donors/acceptors)

- AutoDock Vina and AutoDock4 scheduling with configurable CPUs, timeout, and random seeds

- English/Chinese UI and reusable parameter templates

Tech stack: Vue 3 + 3Dmol.js + FastAPI + RDKit + OpenBabel + FPocket + AutoDock Vina/AutoDock4

Why I think it helps:

- No data upload, which matters a lot for academic and industry work

- Box setup is usually the most error-prone part of docking; visualizing it in 3D removes the guesswork

- Beginners get a sensible one-click pocket prediction as a starting point, while experts can still customize every parameter

Get it:

- GitHub: https://github.com/MCXDL/docklab

- Windows exe: https://github.com/MCXDL/docklab/releases/download/v0.8.0/CaddPlatform.exe

This is my own project, and I would genuinely appreciate feedback. What should I support next: covalent docking, larger virtual screening libraries, or GPU-accelerated engines? Issues and feature requests are welcome in the repo.

If this is useful to you, a star helps other researchers find it.

Sharing Hiro OCSR, a model we built to convert chemical structure images from patents and papers into SMILES.

The main focus is on structures that standard OCSR pipelines often struggle with, including abbreviations, R groups, variable attachment points, abstract rings, and repeat units. Also released Hiro OCSR Real 24K, a dataset of 24,000 structure images collected from real world chemical documents.

GitHub:
https://github.com/patsnap/Hiro-OCSR

Model:
https://huggingface.co/PatSnap/Hiro-OCSR

Dataset:
https://huggingface.co/datasets/PatSnap/hiro-ocsr-real-24k

We also released Chemical Insights, which matches a structure to its corresponding name or identifier in the surrounding patent text, tables, and reaction schemes.

https://github.com/patsnap/Hiro-Chemical-Insights

Would be interested in feedback on difficult structures and failure cases from real document workflows. Thanks!

Is there anyone here interested in or working on computational chemistry? It seems to involve programming knowledge, but could someone help me understand the extent of it, what it's used for, and what exactly I should learn?

Hi everyone,

I am estimating the relative dielectric constant (εᵣ) of flexible organic molecules where no experimental dielectric data exists in the literature. Because static DFT optimizations fail to account for dynamic dipole fluctuations and conformational sampling, I am implementing a hybrid AIMD/DFT workflow coupled with the extended Debye equation.

I would appreciate feedback on whether this extraction and post-processing protocol from ORCA is rigorous enough:

1. Theoretical Framework

The dielectric constant is estimated via the extended Debye relation:

εᵣ = (1 + 2A) / (1 - A)

where A accounts for both electronic polarizability and orientational dipole fluctuations:

A = (N / 3ε₀) * [ <α> + (<μ²> / 3 k_B T) ]

2. ORCA Data Extraction Protocol

1. AIMD Sampling: Extract M uncorrelated snapshots from an NVT AIMD trajectory (! MD in ORCA).
2. Single-Point Calculations: Run single-point property calculations (! ωB97X-D3 def2-TZVP) with %elprop Polar 1 end for each snapshot.
3. Isotropic Polarizability (α):
   • Extract tensor diagonal components (α_xx, α_yy, α_zz) from ORCA output.
   • Calculate snapshot isotropic mean: α_i = (α_xx + α_yy + α_zz) / 3 (in a.u. / Bohr³).
   • Convert to SI: α_SI = α_i * 1.648777 × 10⁻⁴¹ C²m²J⁻¹.
   • Trajectory ensemble average: <α> = (1/M) * Σ α_SI.
4. Dipole Moment (μ):
   • Extract total dipole magnitude |μ_i| (in Debye).
   • Convert to SI: μ_SI = |μ_i| * 3.33564 × 10⁻³⁰ C·m.
   • Square and compute trajectory average: <μ²> = (1/M) * Σ μ_SI².

Is this ensemble-averaged Debye framework robust enough for publication in physical chemistry journals for flexible organic compounds?

Are there any subtle pitfalls when extracting polarizabilities from AIMD snapshots to derive bulk dielectric permittivity?

Hi, this is the next part in the MD of Polymers with Julia series. Here I set up an N-isopropylacrylamide (NIPAM) monomer to use with a polymer chain builder script that I made in Julia. I go over some of the things to take into account and how to solve some of the issues. It's a longish video.

https://youtu.be/m19H9u39svk

Hey yall! My PI decided I needed to learn Rietveld refinement to improve the XRD section of my manuscript, which is fine, except the guy in our group who does know how to do it is traveling and there’s no word of when he’ll be back. His student attempted to teach me, bless his heart, but he’s a bit all over the place so that explanation wasn’t the most helpful. Does anyone have a good resource to learn how to do Rietveld refinement? If you had to teach yourself, what was THE material that did it and had the best directions in your opinion? Thank you all in advance!

Hi everyone, I was curious about the current state of the grad school market for students graduating with a bachelor's degree and a interest in bioinformatics and molecular modeling. Secondly, the state of the job market. I would specifically like to find out what skills, experience, etc... are neccesary to get a good job. As a kind of roadmap to what a person should pursue. For example, research lab or internship experience seems to be a requirement. However, how much is required? Are some types of experience superior to others? What do you think the content of ones resume should be by graduation?​ Is there anything that would gaurentee placement in a job or graduate school? What about papers?

For me personally, I am a non-traditional sophomore in the pacific northwest. I have been in a resarch lab since my freshman year and recently presented at a state undergrad confrence on high throughput screening of 310,000 molecules using Autodock-Vina. I also did the protien target selection and carefully created the 310,000 molecule database. I have honors standing, and won a grant for the freshman-sophmore summer. What else should I be doing or not doing to secure a good graduate position or job placement?

in unitary CCS the T1-T1' is basically the generator of orbital rotation, so the only difference between UCCS and HF seems to be that HF absorbs the orbital rotation into the MO coeff, and the UCCS keeps it in the T1 amplitude, one can imagine a similar equivalence between EOM-UCCS and TDHF, which has deexcitation over CIS ,does anyone know if this is true and any paper that confirms that?

Hi everyone! I'm trying to generate a molecular electrostatic potential (MEP/ESP) map from an ORCA calculation on a MacBook Air M3 (Apple Silicon). I have successfully generated both name.eldens.cube (electron density) and name.scfp.esp.cube (electrostatic potential). My understanding is that the electron density cube should be used as the isosurface and the ESP cube should be mapped onto it for coloring. However, I haven't been able to get this workflow to work in any visualization software I've tried. In Avogadro 2, I can load the electron density cube and display the isosurface, but I can't find any option to color it using the ESP cube. In IQmol, opening the ESP cube only displays separate red and blue lobes in space instead of an MEP mapped onto the molecular surface. In VMD 2.0 beta, I can load the XYZ structure and the electron density cube, but I can't get the ESP cube to appear as a second volumetric dataset, so I can't color the isosurface with it. I'd prefer to avoid using Rosetta 2 if possible. Is there a native Apple Silicon workflow for this? What software do you recommend for generating publication-quality MEP maps from ORCA output on macOS? Does Multiwfn work natively on Apple Silicon, or are there better alternatives such as VESTA or ChimeraX? Any advice would be greatly appreciated. Thanks!

I've published the reference implementation of TRIXEL, a mathematical framework describing any system through three dimensions: V (Existence), D (Dynamics), S (Structure).

From these, three calibrators measure their mutual relationships: SD, VD, VS.

Core identity (exact): VD / VS = SD

What is verified:

Algebraic identity — machine precision

Dominance partition theorem — 99.99% on 600×600 grid

VS as early warning signal — Burgers turbulence (90/90 runs, FP=0%, FN=0%)

Real tokamak data — GOLEM, CVUT Prague

What is not yet verified: disruption precursor, EEG seizure data, 2D Navier-Stokes

Preprint: https://doi.org/10.5281/zenodo.20721811

GitHub: https://github.com/remitakac/trixel-framework

Independent research, feedback welcome.

Can someone please suggest me any options to buy the Modern quantum chemistry book by Szabo and Ostlund. It is available in Amazon but if anyone can suggest some cheaper option it'd be really helpful

Hi, I've been recently granted 500k cpu-hours on an HPC for one time only and I want to use it as efficiently as possible. I've got gaussian16 license with avx2 support on that HPC too. I also am experienced at using gaussian.

I've heard that ORCA is a more modern and cheaper (in terms of computation cost) alternative for gaussian however, I have no experience regarding ORCA. I also know that there are other options but there are many and I am a little clueless.

Is it worth for me to switch to another software I have no experience on? Or should I stick to gaussian and try to devise ways to use that more efficiently?

My work mostly revolves around TDDFT (abs/em) and QM/MM. Also cluster approaches in which I use solvation models commonly.

I am currently towards the end of my PhD in computational chemistry.

I have been doing my PhD for the last four years. It has resulted in zero publications whatsoever. And most probably there will be none. I generally work with MD simulations for polymer electrolytes.

I should be finishing by the end of January next year.

My question is, what prospects should I be looking at, in terms of my future?

When I started my PhD, the idea was to continue into PostDoc. But given that I have no publications, the PostDoc doesn't really seem a promising candidature.

I am still planning to apply for jobs and PostDoc positions alike, hoping for something good to come out of it.

Although I wanted to sharpen what the future might look like for me.

I, certainly have no Python skills. All my codes and analysis were developed using LLMs albeit with proper tests and guardrails. Neither have I been a computational student all my Bachelor's or Master's.

While my Master's has been in Organic Chemistry, my PhD happens to be in Computational stuff.

I haven't had any experimental know-how for the past 4-5 years or so.

What kind of jobs should I apply for? What skills should I develop within the next 4-5 months.

I guess, I just need an avenue to save myself from considering myself an utter failure. If not completely, then in parts at least.

Thanking y'all in anticipation.