• PubChem — 2D structure, molecular formula, molecular weight and the PubChem CID

• ChEMBL — the same, with the ChEMBL ID

• ID lookup — the compound’s identifier in ChEMBL, DrugBank, PDB, SureChEMBL and PubChem at once, together with its InChIKey

If you then choose for example PubChem the structure is displayed (plus other matches) and a link to the PubChem record via the CID, which opens in a new browser window.

Alternatively if you choose the ChEMBL option the structure is shown with a link to the ChEMBL record via the ChEMBL ID.

The ID Lookup option uses UniChem (https://www.ebi.ac.uk/unichem/)

UniChem is large-scale non-redundant database of pointers between chemical structures and EMBL-EBI chemistry resources. Its purpose is to optimise the efficiency with which structure-based hyperlinks may be built and maintained between chemistry-based resources, and is particularly suitable for creating such links ‘on the fly’ (by use of REST web services).

The plugin first searches PubChem to get the structure and the InChiKey, InChIKeys are a hashed version of a full InChI used to facilitate speedier searching. The plugin uses the InChiKey to search UniChem to provide a list of identifiers and links to the appropriate data sources. UniChem actually has links to many more data sources but I’ve only included a the likely most popular.

This was created with the help of Claude Code and I’ve only tested this on my machine (M2 Ultra Mac Studio, macOS Tahoe 26.6.2) Google Chrome Version 152.0.7977.84 (Official Build) (arm64).

Installation instructions

Download the file below, Unzip it, put the folder somewhere permanent (not Downloads), then go to chrome://extensions, turn on Developer mode top-right, click Load unpacked, and select the folder. Full instructions are in INSTALL.txt inside the zip.

Chrome may warn about developer-mode extensions on startup — that’s normal for anything installed this way, choose Keep.

There is an equivalent extension for Safari here

• PubChem — 2D structure, molecular formula, molecular weight and the PubChem CID

• ChEMBL — the same, with the ChEMBL ID

• ID lookup — the compound’s identifier in ChEMBL, DrugBank, PDB, SureChEMBL and PubChem at once, together with its InChIKey

If you then choose for example PubChem the structure (the image is from PubChem) is displayed (plus other matches) on a card beside your selection, and a link to the PubChem record via the CID, which opens in a new browser window.

Alternatively if you choose the ChEMBL option the structure is shown with a link to the ChEMBL record via the ChEMBL ID together with any other matches.

The ID Lookup option uses UniChem (https://www.ebi.ac.uk/unichem/)

UniChem is large-scale non-redundant database of pointers between chemical structures and EMBL-EBI chemistry resources. Its purpose is to optimise the efficiency with which structure-based hyperlinks may be built and maintained between chemistry-based resources, and is particularly suitable for creating such links ‘on the fly’ (by use of REST web services).

The plugin first searches PubChem to get the structure and the InChiKey, InChIKeys are a hashed version of a full InChI used to facilitate speedier searching. The plugin uses the InChiKey to search UniChem to provide a list of identifiers and links to the appropriate

Compound Lookup for Safari – installation

1. Download and Unzip this archive and drag “Compound Lookup.app” into your Applications folder.
   It needs to stay there: Safari loads the extension from the app.
2. Open “Compound Lookup.app” once. A small window appears – that’s all it does.
   It is signed and notarised by Apple, so it opens without a security warning.
3. In Safari: Settings > Extensions, and tick “Compound Lookup”.
4. The first time you use it, Safari asks whether the extension may access websites.
   Allow it. It needs PubChem (pubchem.ncbi.nlm.nih.gov) and EMBL-EBI (www.ebi.ac.uk)
   to look compounds up, plus the page you’re reading to show the result card.
   You can change this later under Settings > Extensions > Compound Lookup.

A few notes

Selections are cleaned up as you’d expect, so a name grabbed mid-sentence with a trailing full stop or wrapping quotes still resolves — while names that genuinely contain punctuation, such as (R)-ibuprofen, 2,4-D and N,N-dimethylformamide, are left intact. Only the text you select, and only to the database you picked from the menu is sent. This only works for html pages not displayed pdf.

The card says which kind of match you got. A weak one is labelled “uncertain” with a link to the database’s own search. In ID lookup, a source that holds only a close relative is flagged — ibuprofen’s PDB entry is marked “different stereochemistry”, because the PDB holds the single enantiomers rather than the racemate — and a source holding nothing says so, because absence is informative too.

Compound data and structure images come from PubChem (NIH/NLM/NCBI), ChEMBL (EMBL-EBI) and UniChem (EMBL-EBI), through their public APIs. This extension is an independent tool and is not affiliated with, or endorsed by, any of those organisations.

There is an equivalent extension for Google Chrome here

It now includes

Yo install

conda install -c conda-forge rdkit
pip install mlxmolkit-rdkit

The main focus is expanded support for inorganic compounds and complexes, including haptic bonds and multi-center attachments, their use in formula and mass calculations, and perspective ring templates. This is part of the continuing aim to build new capabilities for inorganic chemistry.

Additional highlights include expanded SMARTS support, automatic lone pair placement, a new pKa estimator, and numerous improvements to drawing, tables, rendering and file exchange. This update also includes a number of bug fixes.

Full details are available here https://ichemlabs.com/news/read?post=cd2d12_12_released

AlvaSketcher is a modern molecular editor for drawing, editing, and preparing 2D chemical structures.  It is available for macOS and runs on both Apple Silicon and Intel Macs.

It has a modern interface with all the usual chemical drawing tools readily to hand. AlvaSketcher is freely available both for academic research and commercial applications.

There is also a short (1 min) tutorial on YouTube which shows how intuitive it is to use. Skecthes can be saved in SMILES or sdf format. You can also copy as SMILES (and sdf or image) which is very useful for pasting into other cheminformatics tools. I tried to open a few different file formats, and in addition to SMILES and sdf, it opened .mol and .mol2 files. This is intended as a 2D sketcher so files containing 3D structures don’t display ideally. For files containing multiple structures the first structure is loaded.

Updated

Alvasketcher has just been updated ! (Congratulations on being so responsive) and it now includes R-group support.

dock-postprocess is an open-source structure-based drug design toolkit for standardizing docking results and carrying them through restrained OpenMM minimization, pose QC, protein-ligand interaction analysis, ligand conformational strain analysis, and integrated design prioritization.

The workflow is designed for practical docking postprocessing where preserving the docked binding geometry is important.

It supports conventional protein-ligand complexes as well as multichain receptors such as molecular-glue and PROTAC ternary complexes.

All code is on GitHub https://github.com/averysader/dock-postprocess

Mac mini has either M5 Pro chip or M6 chip

M5 Pro

• 15‑core CPU with 5 super cores and 10 performance cores, 16‑core GPU with Neural Accelerators, 16‑core Neural Engine, 307GB/s memory bandwidth
  Configurable up to 64GB unified memory
• 18‑core CPU with 6 super cores and 12 performance cores, 20‑core GPU with Neural Accelerators, 16‑core Neural Engine, 307GB/s memory bandwidth
  Configurable up to 64GB unified memory

M6

• 12‑core CPU with 2 super cores, 4 performance cores and 6 efficiency cores, 12‑core GPU with Neural Accelerators, Dual 16‑core Neural Engine, up to 170GB/s memory bandwidth
  Configurable up to 32GB unified memory

Mac Studio

Mac Studio has either M5 max or M5 Ultra chips

M5 max

• 18‑core CPU with 6 super cores and 12 performance cores, 32‑core GPU with Neural Accelerators, 16‑core Neural Engine, 460GB/s memory bandwidth
  36GB unified memory
• 18‑core CPU with 6 super cores and 12 performance cores, 40‑core GPU with Neural Accelerators, 16‑core Neural Engine, 614GB/s memory bandwidth
  Configurable up to 128GB unified memory

M5 Ultra

• 30‑core CPU with 10 super cores and 20 performance cores, 64‑core GPU with Neural Accelerators, 32‑core Neural Engine, 1.2TB/s memory bandwidth
  Configurable up to 256GB unified memory
• 36‑core CPU with 12 super cores and 24 performance cores, 80‑core GPU with Neural Accelerators, 32‑core Neural Engine, 1.2TB/s memory bandwidth
  Configurable up to 512GB unified memory

All come with a variety of storage options.

I usually go to Arstechnica for a review

The devices’ popularity for production inference took off after macOS 26.2 shipped last December. According to Apple’s release notes, 26.2 enabled “low-latency communication between Thunderbolt 5 hosts for use cases including distributed AI inference using MLX.” Thunderbolt 5 is a very fast wired data connection, and MLX is an open source array framework designed to help machine learning workflows take full advantage of the M-series chips’ unified memory.

This is baseline model a single task CheMeleon model trained on pEC50 data curated from ChEMBL for PXR. It is a no split model, meaning it has been trained with no data allocated to validation and test sets and with just a training set of 1.0.

Instructions for installing and running the pxr-chemeleon-v1 model, which predicts PXR bioactivity for a set of compounds, using OpenADMET’s Anvil framework (openadmet-models).

Prerequisites

Before you start, make sure the following are installed on your computer:

• git — used to download the model and code
• git lfs — a git extension needed to download the (large) model files
• conda or mamba — used to create an isolated Python environment

You can check whether you already have these by running:

git --version
git lfs --version
conda --version   # or: mamba --version

If any of these commands fail, install the missing tool before continuing.

Git is automatically installed with Xcode Command Line Tools. If Git is not installed, entering git in the Terminal will trigger a Mac dialog asking if you would like to Install Xcode Command Line Tools. Click “Install” to begin the download and installation process.

Use Homebrew to install git lfs,

brew install git-lfs

Instructions for installing conda https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html

Installation

Open a terminal and work through the following steps in order, running each
command one at a time and waiting for it to finish before moving to the next.

Enable Git LFS, this only needs to be done once.

git lfs install

Download the model

I created a folder called PXR and moved into it

cd /Users/chrisswain/Projects/PXR

Then cloned the model into the folder

git clone https://github.com/OpenADMET/openadmet-models

cd openadmet-models/

Then created the conda environment

conda env create -f devtools/conda-envs/openadmet-models.yaml

Then activated the environment

conda activate openadmet-models
pip install -e .

I then ran a calculation using full paths to files provided.

openadmet-models % openadmet predict \
    --input-path /Users/chrisswain/Projects/PXR/pxr-chemeleon-v1/compounds_for_inference.csv \
    --input-col OPENADMET_CANONICAL_SMILES \
    --model-dir  /Users/chrisswain/Projects/PXR/pxr-chemeleon-v1/anvil_training/ \
    --output-csv  /Users/chrisswain/Projects/PXR/pxr-chemeleon-v1/predictions.csv \
    --accelerator cpu
    
INFO     Finished prediction                                                                             
INFO     Predictions saved to /Users/chrisswain/Projects/PXR/pxr-chemeleon-v1/predictions.csv  

To run your own prediction you need to edit the path to the input file

–input-path /Users/chrisswain/Projects/PXR/pxr-chemeleon-v1/compounds_for_inference.csv

and the name of the column containing the SMILES

–input-col OPENADMET_CANONICAL_SMILES

If you want to use the gpu edit

–accelerator cpu

If you are also interested in docking ligands into PXR this PYMOL session is a great start. https://macinchem.org/2026/08/07/openadmet-pxr-challenge-pymol-session-file/

There are now three pipelines

1. Molecular Clustering — Morgan FP → Tanimoto similarity → Butina clustering
2. 3D Conformer Generation — DG (4D) → ETK (3D) → MMFF94 optimization
3. PM6_D semi-empirical SCF — full d-orbital NDDO (S/P/Cl/Br/I) with PM6-D3H4 corrections

Full details are on GitHub https://github.com/guillaume-osmo/mlxmolkit

What’s new

Semi-empirical SCF on Apple Silicon — 7 methods (RM1, AM1, PM3, PM6, PM6_SP, PM6_D, AM1*) plus PM6-D3H4 post-SCF corrections — bit-exact to PYSEQM for PM6_D, with no PYSEQM/PyTorch runtime dependency. Every entry point is covered by tests/test_{methods_api,pm6_d_native,pm6_d3h4,pyseqm_port,rm1_scf}.py (83 tests total).

Requires macOS with Apple Silicon (M1/M2/M3/M4). RDKit is needed for molecular input:

conda install -c conda-forge rdkit
pip install mlxmolkit-rdkit

Cambridge Cheminformatics Network Meeting
Hybrid Mode – at the CCDC on Union Road, Cambridge and Online (via Zoom)
Direct Zoom registration: https://cam-ac-uk.zoom.us/meeting/register/NBe00sdHRTOIC1pWXqPcyg
For further details please visit: https://www.c-inf.net
Special Edition – ‘AI in Chemistry Warm-Up Event’, just the evening before!

Programme

Symmetries & Efficient Representations for Generative Structural Biology Models
Alvaro Prat, Oxford University

Automated Extraction of Experimental Datasets from the Literature: Are Agents the Answer?
Ben Honore, Imperial College London

ANNalog: Generation of MedChem-Similar Molecules
Dave Dai, Queen Mary University of London

CheMeleon is Robust but Un-improvable: Where to Next?
Jackson Burns, Massachusetts Institute of Technology (MIT)

RSC CICAG Quantum Computing in Chemistry: Current Capabilities and the Road to Utility
Thursday 19 November 2026, Burlington House, London.

Confirmed Speakers
Matthias Degroote – Boehringer Ingelheim – Quantum computing for drug design

Maria-Andrea Filip – University of Cambridge

Chiara Leadbeater – University of Cambridge – First-Quantised Electron Scattering Simulations

Sabrina Maniscalco – Algorithmiq

Ivan Rungger – NPL

Submit abstracts here

Registration here