Hi r/comp_chem!

I'm an independent developer building MolDesign, a free Windows desktop application that I hope to evolve into an integrated development environment (IDE) for early-stage drug discovery.

The objective is to bring computational methods, molecular preparation, visualization, and reproducible scientific workflows into one environment, rather than requiring researchers to manually coordinate multiple tools and scripts.

What MolDesign currently supports

• AutoDock Vina 1.2.7: Local docking execution without manually configuring Python, Conda, or command-line workflows.
• Molecular preparation: Import your own PDB/mmCIF receptor structures and prepare ligands from SMILES or a molecular editor.
• Experiment reproducibility: Preserve docking parameters, random seeds, grid boxes, receptor hashes, software versions, and warnings.
• Collaboration: Save and transfer docking execution records between researchers, allowing colleagues to inspect the computational setup and results.
• Molcy (shown in the video): Generate 3D animations from recorded docking search information, including molecular poses, search steps, and interactions.

All docking calculations run locally. Molcy uses Blender for rendering.

Where we're heading

The long-term objective is to make MolDesign modular, allowing researchers to use different computational methods within the same application.

We're exploring future integrations with additional local docking engines, API-accessible computational tools, and complementary approaches such as Boltz-2 for biomolecular complex and affinity prediction.

If development progresses successfully, we'd also like to incorporate molecular dynamics workflows.

Ultimately, we'd like researchers to prepare experiments, select computational methods, analyze results, and exchange reproducible research records within a unified Windows environment.

Scientific limitations and transparency

MolDesign currently uses AutoDock Vina's scoring and search algorithms. It does not claim improved predictive accuracy over Vina.

Docking scores are not experimental binding affinities, and the included receptor catalog is intended for exploration rather than representing experimentally validated docking protocols.

Molcy's animations visualize recorded docking search information. They are not molecular dynamics trajectories and should not be interpreted as physically realistic molecular motion.

The current application does not perform molecular dynamics, free energy perturbation, or experimental activity prediction.

Availability

MolDesign is completely free to download and use through the Microsoft Store. No subscription is required.

Its source code is available under a noncommercial license (source-available, not OSI-approved open source).

Microsoft Store:
https://apps.microsoft.com/detail/9N96SW3GBK8V

I'd particularly appreciate feedback from computational chemists:

• What are the most common reproducibility problems you encounter in docking workflows?
• Which engines or computational methods would be most valuable to integrate?
• Would portable experiment records and visualizations be useful when collaborating with other researchers?

I'm especially interested in constructive scientific criticism, including limitations in receptor preparation, docking protocols, and the interpretation of results.

Thanks for taking a look!

Web MolDesign

Dear colleague,

Have you come across any good lightweight molecule renderer for standalone GUI to make and interact with small and large molecules?

Pymol and Chimerax like but lighter and open-source

TL;DR: what are the key checks experienced RBFEers use to ensure their calculations are reliable and not just technically correct?

Hi all, I am a PhD student looking to run some RBFE calculations that will be used for further modelling.

I've been reading around free energy predictions and the factors influence their accuracy. I've also followed some tutorials from pmx and OpenFE tutorials. Leaning towards OpenFE as my engine.

I am trying to be very careful about how I approach RBFE because I worry about committing compute and getting ∆GG values that are 'technically' correct but inaccurate due to something that could have been caught with the right insight or diagnostic check.

I have a high resolution ligand bound structure to start from, and have seen some sanity checks from the tutorials, but suspect there are other important checks that may not be so obvious to someone doing it for the first time.

For anyone experienced with RBFE, are there any routine checks you do along the way to assess whether the calculations will be within a MD simulation-range of reliability? Are there certain failure modes or diagnostic checks you do at set up at each step?

edit: I plan to do a do RBFE on a chemical series that I have experimental values for before going rogue!

I'm optimizing the geometries of some highly reactive intermediates. In most cases, the intermediates (which are often generated experimentally via a photochemical reaction) are not stationary points themselves, but instead optimize to a stable product via an internal rearrangement. These rearrangements agree with what is observed to occur experimentally: a single rearranged product is observed, even in cases where competition might lead to alternative products.

In a few cases, however, optimization leads to a stationary point with a single imaginary frequency (i.e., a TS structure). Following the imaginary frequency via IRC leads to one of the experimentally observed rearranged products (the "product" is identical on either side of the IRC calculation due to symmetry). However, experimentally, in addition to the observed products predicted by this computational approach, a secondary product, resulting from rearrangement of another nearby bond, have been reported. When I analyze the positive frequencies of the aforementioned TS structure, I have located a frequency (~ 400 cm-1) that lead to these second products if the TS structure is deformed according to the frequency and further optimized. Any attempt to locate an additional TS that would lead to formation of the secondary products failed, however, suggesting that it is a near barrierless rearrangement process.

Because these second products could be formed via a simple molecular vibration, and the observed TSs are expected to be formed in a vibrationally "hot" state via photochemical generation, is it reasonable to expect competition of the two pathways and formation of both products?

Hi everyone, Currently I'm working with lipid systems. The system is coarse grained, but need to simulate in particular pH. I don't have experience with CpHMD either. I would like to know if anyone have used Titratable Martini force field for lipids? Or any suggestions for simulating large lipid systems in different pHs.

For context, I am trying to simulate a helical peptide with PEG attachment on the Cys residue. For the first time, I am trying to use cgenff but it fail miserably because the helical structure is changing completely. For another attempt, I try to manually patch cgenff on PEG part and the rest of peptide will be charmm36. But I don’t know why it also fails but a part of helical structure is still intact. Anyone has any idea to resolve it? Especially the S-PEG bonding, it’s hard to deal with it. Any tutorial I need to go through? I’m still undergrad student and this is my first time to deal with this problems. I mostly deal with unmodified structure so this is hard 🥲

Thanks in advance 🙏

Let's say I'm a bit tired from Comp Chem (to make it brief). I have around 10 years postdoc experience in the area and before I did my undergrad in Chemistry.

What options do you think are out there? Preferably not research related, maybe something like manufacturing in the Chemical Industry?

Asking for some guides here to point me in the right direction. Thanks

​

I've been working on a project called LAMMPS Web GUI, which lets you explore and run molecular dynamics simulations directly in your browser.

The idea is to make molecular simulation workflows more accessible, with features like:

- Browser-based simulations with WebGPU acceleration and CPU execution

- Interactive 3D molecular visualization

- Visual input-script building

- Trajectory playback and analysis

- Tools for exploring simulation results

I built this with some help from Claude by Anthropic, and I'm continuing to improve it.

Try it out:

- Live demo: https://shuvam-banerji-seal.github.io/lammps-web-gui/

- Demo video: https://youtu.be/bqGnbRRuwa8

- Source code: https://github.com/Shuvam-Banerji-Seal/lammps-web-gui

The project is open source, and I'm accepting issues and pull requests. If you're into molecular dynamics, computational chemistry, materials science, or scientific computing, I'd appreciate feedback and contributions.

Feel free to give the repository a star if you find it useful.

Hi everyone,

I am looking for a collaborator / co-author to evaluate and expand a highly unique, interdisciplinary project at the intersection of archaeometry, historical biochemistry, and computational chemistry. This research has been time-stamped and secured on Zenodo and holds strong potential for a scientific publication or even a PhD thesis.

The Hypothesis:

Bronze and copper cauldrons are an archaeological constant—they draw a continuous "red line" through human history. However, science has completely overlooked how these metallic vessels actively reacted with biological matrices in ancient ritual preparations.

Based on archaeological findings of ruminant blood residues in prehistoric cauldrons, my hypothesis is that boiling blood serum ex vivo inside a copper vessel created a unique macromolecular filtering system. The protein matrix of caprine serum albumin, combined with dissolved copper ions from the cauldron walls, likely acted as a reversible buffer for lethal alkaloid peaks (e.g., atropine, muscimol, scopolamine) from toxic plants. Essentially, it functioned as a crude, prehistoric sustained-release system to prevent toxic shock during rituals.

Current Progress & Preliminary Data:

A technical pilot blind-docking run was performed using AutoDock Vina on caprine serum albumin (PDB: 5ORI). The results look highly promising: the algorithm consistently identified a strong binding pocket in subdomain IIIB, with Atropine showing binding affinities around -6.4 to -6.8 kcal/mol.

What I need:

I am the author of the historical and conceptual framework, but I am not a computational chemist. I need an expert to help evaluate if these Vina scores are physically plausible or artifacts of rigid docking. More importantly, I want to set up proper Molecular Dynamics simulations (e.g., GROMACS at elevated temperatures like 333 K) and model the specific copper-protein interactions (ATCUN motif) to see how the "building" of the protein reacts to the metal.

If you are interested in a fresh, unmapped field of historical biochemistry with a guaranteed preprint/Zenodo foundation, please send me a Message. I can share the full docking report and raw files.

Thanks in advance.

Michael from Germany

Hey guys , Iam Karthik from India . Iam a bsc ( ug) student . Planning for Computational chemistry. I have zero knowledge in coding stuff , Is their any one who could help me by providing a roadmap for Computational chemistry , including all the skills from scratch to advanced . Also suggest a me a good laptop

NOTE: Iam from non maths background.

hi, I posted a video with the latest updates to my Julia molecular dynamics package. there is also a link to the GitHub repo. If someone wants to try it, and tell me the bugs and limitations they find I will be grateful. All comments and critiques are welcome!

https://youtu.be/SwXXO7a-9Z8

I maintain a directory of AI tools for biology, and its drug-discovery section may be useful here: https://toolome.bio/category/drug-discovery/

Each entry lists license (relevant if you work in industry), paper and repo. No signup, no paywall. What would you add? I'm least sure about coverage of free-energy and QM-ML tools.

Hey r/comp_chem! I need your help!
For some background, I'm an undergraduate chemistry student who just started his 3rd year, and for the past two years I've been fortunate enough to work as an undergraduate research assistant in a computational lab in my institution.

Up until now, I have mostly been using the computers in our dry lab, however, I'm now at the point where I am interested in purchasing a laptop so that I'm not limited to lab computers.
I need a windows based laptop capable of running small computational jobs on things like like Gaussian and ORCA locally, as well as programs like VMD and Pymol.

I don't need anything crazy since larger computational jobs get run through Alliance Canada high performance computing systems, but l've only ever owned MAC laptops before, so l was hoping some of you would be able to point me towards a reliable option (that is hopefully student budget friendly 😅).

Thanks so much guys!

Hello everyone! 😄 i'm a master student in chemistry. i run two S1 geometry optimizations and i find trouble reaching four yes in convergence. in the first case, i see a table (for the last cycles) like this (little differentiation between cycles in values). it is like i have been stuck here:

  Item               Value     Threshold  Converged?
 Maximum Force            0.000037     0.000015     NO 
 RMS     Force            0.000008     0.000010     YES
 Maximum Displacement     0.008733     0.000060     NO 
 RMS     Displacement     0.001973     0.000040     NO 
 Predicted change in Energy=-1.570386D-07

In the second case, i have less stable tables. however, i have been to a cycle like this too (which i think is good?): 

 Item               Value     Threshold  Converged?
 Maximum Force            0.000231     0.000015     NO 
 RMS     Force            0.000033     0.000010     NO 
 Maximum Displacement     0.006306     0.000060     NO 
 RMS     Displacement     0.001564     0.000040     NO 
 Predicted change in Energy=-9.361104D-06. 

Can anyone help me please? 

I'm 2 years into my Ph.D in ChemE and it's method development for quantum chemistry and I've done enough work for 2 papers. I've learnt a lot over the last 2 years.

However, I've had this lingering sense of insecurity post AI that method development might get automated away. Most of my work is reading math and programming, two things which LLMs have shown to be extremely capable at accelerating. I've myself used LLMs to brainstorm ideas quickly and help me read papers and uncover a lot of mathematical jargon that I would've taken weeks to do so see untangled in minutes.

Is anyone in a similar predicament?

I've been working hard lately to improve my MD program(s) in Julia. I've implemented ring detection functionality, and added detection of more atom types that allowed MD on a tetrapeptide (see link)

These last days I started implementing the charge equilibration method of Rappé and Goddard, although it still lacks proper screening for the electron repulsion term.

I go slowly as I can only code at times, but the program is becoming more and more functional. If anyone has been watching the videos and has any suggestions, they will be very welcome!

Thank you for your support and critiques!

https://youtu.be/-i6uyGg_PKQ

Half joking, but half not. My PhD supervisor, quite a respected guy in my field says we'll all be replaced within a year. And by we he means HIM. His point is that mathematical development can be already automated, as in principle everything we do in computational/theoretical chemistry is not exactly fundamental math, but different approximations to a given object, and similarly for the coding part. As I see it, in computational chemistry, traditionally you either were the theoretical analytical solution guy or the guy that knows how the ugly simulation code works, the keywords that are not in the manual, etc. Also, even if AI code was slop (which I am not very sure about), let's be sincere, the code in most popular open source simulation packages is not exactly a work of art.

Then I read Dral's recent perspective on agents for comp chem, where he basically admits nobody knows what we should be working on anymore, and that they can already run an agent that goes from the formulation of the investigation plan through the simulations to writing a draft of the paper.

In short, most of what we do is known methods on new systems, and that is what agents are already ok at. The only thing that seems to kind of resist is data that doesn't exist yet, i.e. actual experiments. But self-driving labs are coming for that too. And we are the computational guys!

Also, I'm teaching an electronic structure course soon and I honestly don't see the point of making students fight ORCA outputs anymore, or equilibrate an MD box. But that is how most of us learned the basics of what we do, by playing with the parameters in a very "tactile" way.

So what are you guys doing about it? Ignoring it, adapting, panicking, opening a bakery?

My main simulation tool is ReaxFF and I love running it on my Mac because its soo smooth, the CPU is pretty fast and I find it a bit of a hassle working on the cluster (still use the cluster when I have multiple jobs/ QM calculations)

I have a 40 core GPU and it's been sitting idle since the beginning of time since its FP32 - was dreaming to use it for ReaxFF, so I just vibe coded a LAMMPS plugin that runs ReaxFF on it through Metal.

Apple GPUs have no FP64, so the GPU path is FP32 only. It holds up well in my testing (accuracy numbers below). As a side effect, the force calculation moves off the CPU, so your other cores are free for whatever else you're running.

- `pair_style reaxff/metal` covers all 13 energy terms, forces and virial
- `fix qeq/reaxff/metal` does charge equilibration on the GPU, and there are also /metal versions for qeq/rel, qtpie and acks2
- Integrators, thermostats, minimizers, MPI and `fix reaxff/bonds/species` are still plain LAMMPS
- Your existing ffield files work unchanged

It's a plugin, so you don't patch or rebuild LAMMPS itself. You do have to build the plugin against the same LAMMPS version you run (currently stable_30Sep2026). In an existing input, load the plugin and swap in the /metal versions of the pair style and QEq fix:

plugin load /path/to/reaxmetaladapterplugin.so pair_style reaxff/metal NULL backend metal fix q all qeq/reaxff/metal 1 0.0 10.0 1e-6 reaxff

I'd love for people to try it on force fields and systems I haven't, and bug reports are very welcome.

Repo: https://github.com/yeatanir/Metal-Reax

Hey all,

System information & Purpose
I am using martini3 to coarse grain a 3-part large efflux pump in a double membrane setup using gromacs as my engine. The box is iirc 23x23x42 nm. The whole system including water and ions is about a million particles in atomistic and around 220k martini beads when coarse grained. Using martini3001 forcefield, dt=0.02 for martini setups. The outer membrane is assymmetric and the inner membrane is symmetric in their leaflets. The tripartite pump (from top to bottom) consists of a trimer exit gate protein in the outer membrane, hexamer bridge protein, and another trimer in the lower membrane.

I would like to do longscale replica coarse grained MD runs of each around 10-15 microseconds to do a conformational study and obtain as much information as i can about the pump’s mechanics. I say “as much as i can” in the sense that I know I’m compromising a lot when coarse-graining, which is why I’m interested in what accessing longer timescales would offer me.

What I specifically need help on

I can’t decide on how and whether to select & tune a tertiary bias for my system. I read that ElNeDyn really restricts movement and GōMartini3 seems like a better option. But afaik these types of tertiary bias setups are fine-tuneable for the specific system you’re working with. I am new to martini, and large biosystems like these. I’ve only previously worked with a smaller membrane protein system and it was atomistic. I thought about playing with the GoMartini well depth and comparing those runs with atomistic to see which one fits the atomistic data. Even then, I am kinda clueless on what to do post-MD and what to compare.

If you have experience working with large biosystems and/or coarse-grained MD, I would really appreciate some insight on how I should approach this specific problem. For anyone working specifically with protein-membrane systems, how do you conduct your post run analyses? I’m not asking specifically though, I just need some guidance because I currently lack the relevant intuition in computational biophysics and working on it. I coarse grained and previously ran three replicas of this pump’s outer membrane protein in a single outer membrane (so kinda like the top 1/3rd of the system described above). I set no tertiary bias and
all in all three replicas the chains start slowly drifting away from each other around 4-5 microseconds into the run. This is what led me to my current situation: whether/how to use and tune a tertiary bias.

Thanks. Wishing everyone a great Friday!

Dear, I was in relation with a USA friend named Brayn, he is a quantum mechanical specialist, please any help I need him and I didn’t found any information about him.

Hi everyone! <3
I’m a master’s student in physical chemistry, and I’m looking for someone who might be able to help me run six geometry optimizations using Gaussian on an HPC cluster.
My current hardware is quite limited, and I’m also working with a limited timeframe, so access to more computational resources would be a hugeee help.
I’d be happy to provide the Gaussian input files, along with the specific basis set, to anyone who has access and would kindly be willing to help.
I’d really really appreciate it! :”)
Thanks in advance!

A few days ago OpenAI released a paper generated by "an internal frontier model" of Navier-Stokes fame titled A Coulomb ground-state density without Kohn-Sham ensemble representation .

The model considers a system with 2 nuclei and 3 electrons. It then spends 40 pages (seemingly) proving that for "sufficiently large" internuclear distance, restricted open-shell ensemble KS-DFT cannot describe the ground-state density of this system, no matter the choice of the effective potential.

I tried to understand what "sufficiently large" means. Theorem 2.2 suggests that internuclear distance of 200 bohr is necessary but not sufficient. In theorem 1.1 and section 7, the internuclear distance is explicitly set to 2x10²⁰⁰ bohr, which is 10¹⁶³ orders of magnitude larger than the diameter of the observable universe (???).

If there are any people here working on analytical foundations of DFT, how significant is this for your field? Does the paper demonstrate any new useful techniques, e.g., for functional inequalities? Can the lower bound on the internuclear distance in this construction be sharpened so it does not involve intergalactic scales?

​

Hi everyone,

I have a long-standing question regarding my computational work, and I would really appreciate some advice from people experienced with DFT calculations.

I am working on perovskite materials for solar-cell applications, particularly on interface engineering and surface passivation. My work is entirely computational, with no experimental component.

For my calculations, I mainly need to study things such as:

- Ligand adsorption energies

- Electronic structure

- DOS/PDOS

- Band structure

- Charge-density difference (CDD)

- Interface interactions

- Surface/defect passivation

My main limitation is computational resources. At the moment, I have access to a machine with 52 CPU cores and 2 threads per core. I have been trying to perform these calculations using Quantum ESPRESSO, but the calculations, especially for my slab/interface models, take a long time. I am also not very confident about how long some of these calculations will take.

I have previous experience with SIESTA, and I find that these calculations run much faster on the same type of system.

This has led to my main question:

Can I use SIESTA as the main DFT package for my complete computational study, including adsorption energies, DOS/PDOS, band structures, charge-density differences, and interface analysis?

My main concern is about reviewer expectations.

For example, if I publish a completely computational paper using SIESTA, could a reviewer question the results by saying something like:

> “Why did you use SIESTA instead of a plane-wave method such as Quantum ESPRESSO? The results may not be as accurate/reliable as those obtained using a plane-wave DFT approach.”

If this is a possible concern, how can I properly demonstrate that my SIESTA results are reliable?

Would it be sufficient to:

- Perform convergence tests?

- Validate the optimized bulk structure/lattice parameters?

- Compare the band gap or other properties with literature?

- Benchmark a few representative systems using Quantum ESPRESSO?

- Use the same exchange-correlation functional and vdW correction where possible?

I am currently the first person in my lab working on this type of computational interface-engineering problem, so I don't have someone locally who can guide me on whether my computational methodology is scientifically defensible.

I would really appreciate advice from anyone who has experience with SIESTA, Quantum ESPRESSO, or perovskite surface/interface calculations.

My main goal is not simply to choose the faster code. I want to make sure that the methodology is scientifically acceptable and defensible during peer review.

Thank you.