r/comp_chem

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Title says/asks it all, but I'll expound.

I'm trying to decide on a concentration for grad school and comp chem is one option I'm considering (along with organic chem and analytical chem).

I can't say I find comp chem extremely interesting, but part of that may just be me feeling overwhelmed because I don't have a strong physics or math (or programming) background.

I try to work on learning more math and programming in my free time, but physics is honestly not my cup of tea. Not even in the slightest.

I'm reading papers from various labs that I feel I may/would want to join and the papers from labs that are mainly focused on using comp chem are the hardest papers to stay engaged with.

In theory, I love the idea of the power comp chem holds, to perform/run many reactions in a short amount of time, and make predictions it would take a long time for humans to decide on.

However, I'm not sure if finding comp chem "cool" and powerful is going to be enough of a motivator to actually gain any competence in the topic.

Plzzz share your experience

I'm currently in my first year of PhD and my laptop is struggling these days. I was wondering if anyone can. Recommend a laptop. All calculations are done on an external HPC but I need something that can handle several windows open with molecules, word, excel, ect ect.

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I'm a bsc chemistry graduate. Is there any course to make 50-60k rupees per month as a starting salary after Msc chemistry. Is pursuing msc chemistry could give me such job?

I made a small VS Code extension for visualizing VASP structure files (POSCAR / CONTCAR / .vasp) directly inside the editor.

This mostly came from my own frustration of repeatedly exporting structures to VESTA just to quickly check edits while working in VS Code, so I thought I’d share it in case it’s useful to anyone else with a similar workflow :)

The preview updates automatically as the file changes and currently supports basic bond visualization, atom hover coordinates inspection, and selective dynamics highlighting.

VS Marketplace:
https://marketplace.visualstudio.com/items?itemName=PurunSimonCao.atomview

GitHub:
https://github.com/Simon-Cao-Git/AtomView

Hi all,

I am currently designing de novo proteins based on a wild type. I want to use MD to check if the mutations I am adding are likely to have destabilised the protein. This is so I can have a rationale for reducing my design space, and prioritise variants to express at scale.

My proteins are approx 97 amino acids long.

I am currently in a production run cycle - my plan is to run 5 x 200ns simulations for each protein variant and compare RMSD, Rg, RMSF, and hydrogen bond number throughout the run to infer improved or reduced stability. RMSF for functional regions specifically I.e. what do the variations do to rigidity of the scaffold and the functional region.

I have expressed the proteins and will be experimentally validating the runs by testing thermostability and activity of the proteins in vitro.

For people who are fluent in MD - does this sound like something that would hold up and be defensible?

Thanks for your help!

Hi I did a molecular docking run using a tetramer and trimer of a polymer. results show that both trimer and tetramer bonded onto the same binding pocket, what could be the implicaction to this? is this an implication that the protiens active site prefers bigger molecules

Hi,

I would like to perform metadynamics to a gpcr bound in a lipid bilayer to a protein ligand which I docked to the receptor. From a paper I know the structural differences between the active and inactive receptor.

From what I understand would be good practice to:

- Show that running unbiased MD does not show the activation of the GPCR.

- Run also the receptor without any ligand to show the energy difference with and without the ligand

- Run a negative control with a protein who supposedly does not activate the receptor

- Run the MD in triplicates.

Since keeping up with all these practices would mean a lot of computational power that since I am using my university HPC that implies a lot of queuing and stuff. How long should i run unbiased and meta md? Should i do triplicates? Is it really important to run a negative control?

And for the one experienced in metaMD, how do i pick a CV that makes sense? And other tips?

Hi,
I use Orca for quite a time. It absolutely awesome for TDDDFT or post HF when it comes to energies or properties. Yet, what I noticed, it struggles with optimisation of all species other than ground states, especially radicals and anions.

Maybe this is due to the functional I use - M062X and basis set. In Orca I stick with ma-def2 but in Gaussian I’m naturally restricted to Pople’s. The algorithm is struggling so much, I constantly have to change maxstep or trust, and defgrid3 combined with verytightscf which are critical in this combination make it even slower.

I’m a biochemistry graduate considering pursuing a PhD in chemistry focused on either biophysics or cheminformatics, with the goal of eventually working in biotech/pharma or at a startup.

My main hesitation is that I’m worried the field could look very different by the time I finish the PhD, either due to oversaturation or major shifts in the industry.

For people currently in the field, how does the job market feel right now? And where do you see computational chemistry / cheminformatics heading over the next 5–10 years?

Anyone here tried to fully build from end-to-end (initial geometry, optimization, calculation, analysis) a comp chemistry pipeline with an AI agent. Either MD or electronic structure.

I’ve been trying to do it so far with Claude for a fine tuning MACE. Results have been mixed, sometimes the agent does everything right with barely any feedback, other times it does some really silly stuff.

Any tips on prompting?

I would appreciate technical feedback from the quantum computing / computational chemistry community on a manuscript I recently submitted.

The work examines whether the physically relevant determinant manifold in structured electronic ground states may be far smaller than the formal Hilbert-space dimension suggests.

Main result:

• FeMoco CAS(54,54)
• formal determinant space: ~3.79 × 10^30
• retained determinants: ~748,000
• single desktop workstation (RTX 4090)
• PT2-based convergence / variational inclusion workflow

The manuscript also includes:

• H₂ and LiH benchmark validation against FCI
• supporting code and data files
• discussion of determinant-space compressibility
• DTQW-guided determinant graph navigation

I am NOT claiming that all many-electron systems are universally compressible, nor that quantum computing is unnecessary.

The point is narrower: for the systems examined here, the physically relevant manifold appears highly compressed and computationally navigable.

I would genuinely appreciate criticism, reproduction attempts, or technical discussion from people working in quantum chemistry, tensor methods, selected CI, quantum algorithms, or many-body physics.

Especially interested in whether similar manifold compression behavior has been observed in other frameworks.

Manuscript and supporting materials:
https://zenodo.org/records/19985028

About 3 years ago while I was in college I decided to start self-studying Biochemistry, but quickly in my journey I realized that the existing sim software out there just didn't cut it for me (either too expensive, too slow, too inaccessible, or maybe I'm just stubborn idk). So I did what any madman would do and started building my own XD.

It's still nowhere near where I plan for it to be, but https://biowareendeavors.com/ is where I am now. It matches Psi4 calculations very very closely, and offers about a 10x speedup on "comparable" hardware (hard to compare hardware between CPU and GPU codes). Everything is ran in the browser and it runs on cloud GPUs. Currently it can be used for point solves, geometry optimization, and BOMD sims. Near linear compute scaling with system size is in active development.

A demo video is linked here, I was just wondering what peoples initial thoughts were on this. Is this something you'd find useful? Would you scrap the browser UI and just want an API or code you can run locally? What features not currently offered would make this useful to you?

Like I said this is nowhere near where I plan for it to be, this is just where we are now having picked up my first QM textbook three years ago :). I built this as a personal tool, but if I can share it and make it useful for others too then I mine as well.

Software engineer here, not a computational chemist. I've been reading about MACE/NequIP/Allegro and am trying to understand the practical experience of actually using these in research flows.

If you've worked with ML potentials (or wanted to and bounced off), I'd love to hear:

- What broke or frustrated you most recently?

- What's your current workaround?

- What's the last thing you tried where you ended up writing custom glue code (or giving up)?

Why am I asking? I'm exploring whether there's a software business that would actually help here, or whether the existing open-source tooling is good enough. Honest answers either way are useful, including "this is a solved problem, move on."

Happy to share what I learn back to the thread. Thanks much!

Hi everyone! I’m currently an Erasmus Mundus master’s student in Chemoinformatics, and I’m now planning my six-month internship, which will form the basis of my master’s thesis.

I’m aiming to pursue a PhD in medicinal chemistry, so I’m looking for a thesis project that is mainly computational (around 90% or more) but ideally includes some experimental validation. I’m not necessarily looking for a synthesis-heavy project, but I would be very interested in work that could include purchasing selected compounds and testing them to generate biological validation data.

I have about five years of experience in computational chemistry and drug design, including building AI/ML workflows and algorithms for research projects. I also have a solid publication record, adapt quickly to new software and research environments, and have several months of experience in organic synthesis.

I would really appreciate recommendations for academic labs, research groups, or industry teams working in computational medicinal chemistry, structure-based drug design, virtual screening, lead optimization, or related areas. If you know of groups that might be open to hosting a master’s thesis student, or people I could reasonably contact, I would be very grateful for any suggestions.

Thanks a lot.

Hi everyone, recently I tried to do my first NMR calculation with ORCA, during my internship I've observed a side product and since I'm having some problems figuring out the structure I wanted to try an NMR simulation for a molecule I think could be said side product and compare it with experimental data. The workflow I used is this:

1. Optimization (of my molecule and TMS):

!B3LYP D4 OPT FREQ defé-TZVP TightSCF CPCM(chcl3)

2)NMR calculation (of the molecule and TMS):

! WB97X-D4 pcSseg-2 NMR TightSCF CPCM(chcl3) %eprnmr Nuclei = all H { ssall } Nuclei = all C { ssall } Nuclei = all S { ssall } Nuclei = all N { ssall } end

3)I created the .nmrspec file (the NMR calculation file were named asdf and the optimization were named asd)

NMRShieldingFile = "asdf" NMRCouplingFile = "asdf" NMRSpecFreq = 400.00 PrintLevel = 0 NMRCoal = 1 NMRREF [1] = 31,65 NMRREF [2] = 189,39 end END

4. I tried to run the orca_nmrspectrum to create the spectrum (or the file to put in gnuplot to have the NMR spectrum), I wrote in the cmd:

orca_nmrspectrum asdf.gbw asdf.nmrspec > output

After a minute I got this error: [file orca_tools/qcmem.cpp, line 1018]: OUT OF MEMORY ERROR!

I don't know where is the problem since I followed the instructions on the manual.

As far as I can tell, there are four broadly available, well-documented, decently optimized, and free quantum chemistry codes out there (ORCA, NWChem, Psi4, and PySCF). This is not including immense number of plane wave/PBC codes, I'm talking quantum chemistry (DFT + correlated methods) in vacuum.

Why so many? What are some of the advantages and disadvantages of each of these packages?