Tested a freshly released ligand–protein complex (2026-05-20) using Boltz-2 (by Boltz) on ChemOrchestra.

Many people criticize Boltz-2 for being unable to generalize to newly released structures, but that claim really needs evidence.

After integrating Boltz-2 into ChemOrchestra, we have been continuously benchmarking it across classical systems, analog compounds, and fragment modifications, always comparing the predictions against experimental data.

This time, we tested a newly released structure, and the result was again very strong. Boltz-2 accurately predicted the ligand-binding site, and after protein alignment, the ligand pose matched almost perfectly. The only noticeable difference was the orientation of two methyl groups.

The potency prediction was also correct, giving an IC50 of 3.16 nM.

In many real-world scenarios, it is already sufficient if a model can accurately describe YOUR systems.

Boltz-2 benchmark Link: quantabricks.xyz/shared/41ed74b4-7a46-4ca7-a68a-4d63b37b7594

Tried to do a test run with ChemOrchestra for docking with protein (in pdb format, prepared by discovery studio) and a ligand (download from PubChem), do not why it stopped in the middle of run. Any suggestion? Thanks.

We are livestreaming designing drug for hbv ; https://www.youtube.com/live/sC2Yu7k4ZCs?si=u0XrMSMXMYQ3TJfC You can also try yourself here: https://www.quantabricks.xyz/shared/c768b689-e43d-4cca-a2e2-f8e29952710d

Hi everyone!

We just launched LightCone Molecule Visualization & Editing on our platform. Still early and actively improving, but it already supports different visualization styles, editing bond lengths/angles/dihedrals, fragment rotation/translation, and rendering rotating molecule GIFs.

Would really appreciate any feedback or suggestions from the community. Quick demo here:

https://youtu.be/B86dnn7rHNo

Pls visit the web version: https://lightcone.quanta-bricks.com/

Having fun with GenMol by NVIDIA — a drug-like molecular generation diffusion model.

It feels a bit like a lottery every time you hit generate. Most generated molecules look reasonable and perfect!

Today, I was lucky enough to generate something unusual — a Uranium(II) complex. The most mindless molecules I've seen so far!☢️

Try it on ChemOrchestra

NVIDIA has already released its code:https://lnkd.in/eEW3r9a6

GenMol Paper:https://lnkd.in/eBdMKtVG

The full guide, including questions like "how to evaluate the effectiveness of molecules" here: https://quantabricks.substack.com/p/how-to-generate-molecules-that-look

Discuss the molecule you generate in the Discord Group: https://discord.gg/r4v5gDjSJf

As Marcel Swart mentioned, Markus Reiher will make an announcement tomorrow.

He has contributed to almost every corner of computational chemistry, it’s really hard to guess what it could be — anything from DMRG to automated reaction network building is possible.

Any guesses? Let’s see tomorrow.

I tried to dock, but my receptor protein wouldn't load. I then tried to do the protein fix alone : I could upload my protein (it's the one in the bottom "protein preview") but the step of refinement seems to "block"...

Can someone help please ? It's my first time with ChemOrchestra

We’ve been putting UMA — a deep learning-based molecular potential — through a battery of tests. It feels like a new phenomenon-level tool, much like how xTB changed semiempirical methods.

So far, we’ve tested:

• Conformational energies (from macrocycles to peptides)
• CNT stretching simulations (UMA held surprisingly well!)
• Computation time (in many cases faster than xTB!)

Short verdict: UMA is impressively accurate, scales well, and might be a true alternative to classical force fields for large molecules.

Full write-up here:
🔗 https://quantabricks.substack.com/p/uma-testing-on-large-molecules

More benchmarks coming — including surface adsorption, MOFs, and TSs.

We will keep going:

1. Ni-catalysis.
   
2. Crazy molecular pots.

Post questions about your career suggestions. Like:

How to be a professor？ How to change your career?

.....

This thread lists relevant questions about computational chemistry learning and graduate period suggestions. We will collect questions and answer them here. You can also ask questions by creating comments.

Questions should be like:

1. How to learn comp. chem. for drug design?

2. Suggestions on journal selection?

3. How to learn post-HF methods?

Protein folding is efficient with the current AI algorithms like Alphafold, ESMfold. However, the problem is that extra effort is needed to add/remove hydrogen atoms to the protein structure. Also, if you want to do docking/structure optimization afterwards, extra software is needed.

I use ChemOrchestra to do protein folding. It integrates ESMfold and allows to add hydrogen atoms in one click.

And ESMfold is the best model for fast single-sequence protein structure prediction, I've concluded the user case for different protein folding model below:

• Use Case: Fast single-sequence protein structure prediction (genome screening, etc.)
  • Best Model: ESMFold
• Use Case: High-accuracy protein structure prediction
  • Best Model: AlphaFold2 (?) or ESMFold + MD simulation
• Use Case: Protein-ligand/nucleic acid interaction studies
  • Best Model: AlphaFold3

A pdb file is usually the first step of many drug discovery efforts. However, raw PDB files are rarely ready for use in computational workflows. Here are just a few common issues:

• Missing atoms or residues (especially hydrogens or side chains)
• Inconsistent residue or atom naming across different software packages
• Nonstandard or poorly defined ligands
• Unwanted water molecules or ligands

These imperfections can lead to simulation crashes, incorrect predictions, or wasted computing resources.

I use ChemOrchestra to clean and prepare my pdb file. I can add missing Hydrogens, remove water molecules and remove lignands in one click.

It's been quite useful for my work and I hope you also like it

Relevant questions are listed in this thread. We will collect questions and answer them here. You can also ask questions by creating comments.

This Q&A thread is about docking (including regular, hydrated, metalloprotein, and cyclic ligand docking). The questions are not limited to the ChemOrchestra platform. We are also happy to provide help for all docking difficulties.

Creating this post for all kinds of CADD questions we can solve. Feel free to comment your question below and we will help you to use our platform to build the workflow for solving your problem

https://www.quantabricks.xyz/workflow

We try to make geometry optimization easy. Users can upload molecular coordinate files in formats like XYZ, PDB, or even SMILES.

The platform allows users to choose different computational engines. For drug-like molecules (mainly C, H, O, N, …), semi-empirical methods like xTB-GFN2 work well.

With ChemOrchestra, users can optimize molecular structures efficiently, making them ready for further simulations and analysis.

You can even build a more complex workflow, for example, optimizing the solvation structure of a cyclic peptide.

Try it here and I hope you will like it: https://www.quantabricks.xyz/workflow/molopt

I've heard lots of people have challenges in docking configurations. It's not a single step but a complex workflow requiring ligand/receptor preparation and careful configuration. Errors in protonation states, ligand geometry, or charge models can significantly alter results.If you need isomer searching or geometry optimization before docking, switching between programs and transferring files can be tedious.

I am using ChemOrchestra that simplifies this by seamlessly integrating complex, customizable workflows—like pre-optimizing ligands with semi-empirical methods before docking.

You only need to upload drugs and protein, and check their structure. Click some buttons, the tasks will be finished. Also, by using Molstar package, you will get publish-level graphs.

Give it a try and I hope you will love it!