In a blog post Autodesk announced an update to AutoCAD with support for Apple Silicon

https://blogs.autodesk.com/autocad/autocad-2024-mac/

For the first time, AutoCAD for Mac 2024 and AutoCAD LT for Mac 2024 now run natively on both Intel and Apple Silicon architectures, including M1 and M2 chips in the M-series chips. The support for Apple Silicon can increase overall performance by up to two times compared to 2023.

A browse around the website also shows that Maya has also been updated.

https://makeanything.autodesk.com/maya.

I'm not sure why it took so long but this will result in a significant performance boost.

Gephi 0.10.0 is here! Download it from https://gephi.org, now supports Apple Silicon, It loads much faster and complex operations such as layouts run smoother.

Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possibilities to work with complex data sets and produce valuable visual results.

Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.

A latest news from iChemLabs highlights Apple silicon support for ChemDoodle 2D and ChemDoodle 3D https://www.ichemlabs.com/news/read?post=applesiliconsupport. As the plot below shows there is a significant boost in performance.

Asahi Linux is a project and community with the goal of porting Linux to Apple Silicon Macs, starting with the 2020 M1 Mac Mini, MacBook Air, and MacBook Pro. More details here https://asahilinux.org/about/.

Asahi Linux is still in very early alpha stages. Lots of hardware components are not functional yet! Check out the Feature Support page first, and if you still want to give it a go, see the blog post for the alpha installer:

The alpha release https://asahilinux.org/2022/03/asahi-linux-alpha-release/.

All the code is on GitHub https://github.com/AsahiLinux.

I've updated the pages comparing the new Apple Silicon machines with those with the older Intel chips https://www.macinchem.org/reviews/MacBooks/m1macbookpromax.php. In addition to the MacBook Pro M1 Max I've now added the M2 MacBook Air.

News just in from ChimeraX team https://www.rbvi.ucsf.edu/chimerax/data/czi-nov2021/apple_m1.html.

We are making a version of our ChimeraX molecular graphics program that runs natively on Apple's new M1 CPUs for faster interactive calculations. We'll report some speed-up timings and describe difficulties porting from Intel to the Apple M1 CPU. A native Apple M1 version of ChimeraX is not yet available, but we expect to release it within 6 months.

Difficulties porting ChimeraX to Apple M1 CPUs

• ChimeraX Python and C++ code needs no changes.
• ChimeraX uses 90 packages developed by others.
• 60 are pure Python from the PyPi repository.
• 30 are binary packages that need Apple M1 versions.
• 6 binary packages do not have Apple M1 distributions: ambertools, h5py, imagecodecs, netcdf4, pytables, scipy.
• Qt 6 window toolkit is distributed for Apple M1 but not Qt 5.
• ChimeraX uses Qt 5, the stable Qt version from 2012 - 2021.
• Qt 6 with html support was released September 2021.
• Apple M1 applications must be either all native M1 binaries or all Intel binaries, no mixing.
• Need to distribute either a large univeral package that includes both Intel and M1 binaries, or two separate ChimeraX versions.

Potential advantages of native Apple M1 ChimeraX

• Better OpenGL driver stability with Apple M1 GPU.
• No graphics driver crashes among 43 ChimeraX bug reports in 2021 with Apple M1.
• About 100 ChimeraX graphics driver crashes reported on Intel Macs in past 2 years.
• Better C++ crash stack traces with native M1 app than with Intel emulation.
• Intel ChimeraX crashes on M1 often give no C++ stack trace.

The 2022.02 release of Chemical Computing Group's Molecular Operating Environment (MOE) software includes a variety of new features, including support for Apple Silicon!

This update also includes

• Browser-based Combinatorial Library Enumeration with on-the-fly reagent search and library generation

• MOEsaic Docking calculations with real-time visualization of results

• scFv and custom antibody homology models

• GPU-accelerated protein modeling and protein-protein docking

• Hydrogen Mass Repartitioning for accelerating MD and Thermodynamic Integration

• Database Viewer SNFG carbohydrate display, graphic objects, and enhanced plotting

If you want to read more about the performance gains using MOE on an M1 Mac have a look at this page https://www.macinchem.org/reviews/MacBooks/moe.php.

 

Mnova has just been updated and it runs on Apple Silicon.

Just to highlight a couple of new features

New Product! Mnova Screen 2D. Efficient Batch Processing Tools for Lead Discovery using Protein-Observed 2D NMR

This product is related to Mnova Screen, which can be used to process ligand-observed 1D NMR spectra (STD, T1rho, CPMG, WaterLogsy, etc.). Screen 2D processes the protein-observed 2D 1H-15N, 1H-13C, or H1-13C/15N dual heteronuclear correlation (HSQC or HMQC) spectra to find binding ligands based on chemical shift perturbations.

Save the Whole Document as JCAMP - Mnova General

An enhancement to the way we handle JCAMP files. We have implemented a new file filter, "JCAMP-DX Document” (*.jdx *.dx *.jcm *.cs *.jcamp), which is analogous to the other JCAMP-DX except for the fact that when saving, it saves the entire document.

SIMCA Model Classification - Chemometrics. Soft independent modelling by class analogy (SIMCA) is a statistical method for supervised classification of data.

In order to build the classification models, the samples belonging to each class need to be analyzed using principal component analysis (PCA), from which only the significant components are retained.

Full details and download link are here

https://resources.mestrelab.com/top-highlights-in-mnova-14-3/

 

A really useful blog post on PyTorch on Apple Silicon

https://sebastianraschka.com/blog/2022/pytorch-m1-gpu.html.

 

PyTorch is now available on Apple Silicon https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/.

In collaboration with the Metal engineering team at Apple, we are excited to announce support for GPU-accelerated PyTorch training on Mac. Until now, PyTorch training on Mac only leveraged the CPU, but with the upcoming PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac.

To get started, just install the latest Preview (Nightly) build on your Apple silicon Mac running macOS 12.3 or later with a native version (arm64) of Python https://pytorch.org/get-started/locally/

 

Whilst Activity Monitor gives a nice graphical display it perhaps lacks granularity

A Python-based nvtop-inspired command line tool for Apple Silicon (aka M1) Macs. Code is available on GitHub https://github.com/tlkh/asitop

• Utilization info: CPU (E-cluster and P-cluster), GPU, Frequency and utilization, ANE utilization (measured by power)

• Memory info: RAM and swap, size and usage, Memory bandwidth (CPU/GPU/total), Media engine bandwidth usage

• Power info: Package power, CPU power, GPU power, Chart for CPU/GPU power, Peak power, rolling average display

asitop uses the built-in powermetrics utility on macOS, which allows access to a variety of hardware performance counters. Note that it requires sudo to run due to powermetrics needing root access to run. asitop is lightweight and has minimal performance impact.

asitop only works on Apple Silicon Macs on macOS Monterey

Install using pip

pip3 install asitop

To activate

sudo asitop

Enter your password and you should see something like this

 

I'm slowly working through a variety of cheminformatics toolkits and computational chemistry applications, I'm trying to run some "real world" workflows so you can see what kind of performance improvement you might expect.

The index page is here https://www.macinchem.org/reviews/MacBooks/m1macbookpromax.php and I'll update it as a test more applications.

 

So the Apple event revealed the new Apple Mac Studio, a double hight Mac mini, when combined with the new M1 Ultra chip this small enclosure appears to deliver really impressive performance.

The M1 Ultra is an evolution of the M1max chip that uses "UltraFusion" technology to fuse two M1 Max chips together, resulting in a huge processor that offers 16 high-performance CPU cores, 4 efficiency cores, a 48- or 64-core integrated GPU, and support for up to 128GB of RAM, 800GB/s of memory bandwidth and a 32-core Neural Engine.

Whilst Apple gave the usual performance tests based on video editing I'm not sure they give a realistic measure of performance for scientific applications.

I've been looking at a variety of different application/toolkits/python scripts etc. on my MacBook Pro M1 max here , and if anyone has a chance to test scientific software on the M1Ultra I'd be happy to include the results.

 

I had a look at building combinatorial libraries using MOE on an MacBook Pro Apple M1 max.

Bottom line it is seriously fast.

Read more here...

 

The latest Schrödinger Software Release 2022-1 brings support for Apple M1 machines in addition to a range of updates and new features.

Hit Identification & Virtual Screening

Pharmacophore Modeling

New alignmulticores.py script to align 3D ligands to a reference ligand with multiple disconnected cores [2022-1] Ligand Docking

Return SMARTS of the core used when running core constraint docking with MCS [2022-1] Input file that generated a Glide grid is saved in the grid archive to improve ease of making changes [2022-1]

Target Validation & Structure Enablement

Protein Preparation

Sped-up hydrogen atom assignment to be o(n) by system size [2022-1] Protein X-Ray Refinement

PHENIX/OPLS supports PHENIX 1.20 [2022-1] Multiple Sequence Viewer/Editor

Automatically save MSV projects [2022-1] Rapid selection of a subset of sequences based on user-defined percent identity or similarity relative to a reference sequence [2022-1] Improved ability to save one or more sequences by ‘right clicking’ to export [2022-1] Protein Homology Modeling

Selectively download only the PDB BLAST subset of the NR BLAST database for local homology modeling [2022-1] New Workflow Action Menu prompts for homology modeling enables single click access to structure quality assessment, reliability reports, additional loop refinement, and sidechain refinement and localized minimization [2022-1]

Platform Environment

Maestro Graphical Interface

Apple M1 Support [2022-1] New 2D Sketcher (beta) [2022-1] New Workflow Action Menus [2022-1] Antibody Modeling Homology Modeling [2022-1] Force Field

Improved accuracy of histidine parameters, particularly in FEP+ prediction of histidine pka’s [2022-1] Improved geometries for B-N bond containing compounds [2022-1] Up to 10x faster execution of FFBuilder when parameterizing hundreds of ligands through greater job distribution [2022-1] Workflows & Pipelining [KNIME Extensions]

New 2D Sketcher node [2022-1] Run from LiveDesign [2022-1]: Export to LiveDesign node can export all the structures so model results can be stored in new LiveReport(s) Model output columns can contain files (eg with pdf) Store an executed workflow in a LiveReport column

Medicinal Chemistry Design

Ligand Designer

Ability to specify a max number of enumerated compounds [2022-1] Added access to “Vendor ID” details in the Project Table for purchasable compounds [2022-1]

Lead Optimization

FEP+

FEP+ Correlation Plot [2022-1]: Display best fit line and equation of the line Modified reporting to show confidence intervals instead of standard deviations Web services [2022-1]: Improved performance when viewing map status Solubility FEP (Beta)

Access to trajectory, representative structures, FEP classifiers in the analysis tab [2022-1] Web Services will return fmp/fmpdb files instead of mae/csv for analysis [2022-1] AutoQSAR

DeepChemAutoQSAR now supports Windows and Mac platforms [2022-1] FPsim-GPU

New vendor column in similarity results [2022-1]

 

A GitHub repository giving details of how to set up an Apple M1 machine for data science. https://github.com/tcapelle/applem1pro_python, includes a series of test scripts for benchmarking.

There is a M1 Max VS RTX3070 Tensorflow Performance Tests here.

 

A couple of readers have asked about the performance of Mathematica on the new Apple Silicon machines. I've heard second hand reports that it runs 2-3 times faster on "real world" problems but no details.

Does anyone have any benchmarks that would be willing to share?

 

Some one just sent me an email mentioning MacVector supports Apple silicon.

MacVector 18.2 requires Mac OS X 10.12 or later. It will NOT work on Windows, Mac OS 9 or on Mac OS X 10.11 or earlier. MacVector 18.2 is a "Universal Binary", meaning it will run natively on both Intel and Apple Silicon based Macintosh computers.

 

When the first M1 MacBooks came out there were limited libraries available but over the last year most of the libraries needed for data science now support the new Apple silicon architecture.

Includes details for installing TensorFlow and the test dataset.

 

Updated

Added a couple more comparisons.

The pages comparing cheminformatics/compchem apps on the MacBook Pro M1max are proving very popular. Several readers have asked me to compare energy usage which is an excellent suggestion.

Based on a suggestion I purchased Nevsetpo Power Meter UK Plug Power Monitor Watts Meter Plug and I've used it to test a selection of tasks. Once plugged into a socket it monitors total energy consumption of anything device plugged in. Both machines were fully charged and the "Optimised battery charging" was switched off.

I tried a few computationally intensive tasks and details of energy consumption are here..

 

The pages comparing cheminformatics/compchem apps on the MacBook Pro M1max are proving very popular. Several readers have asked me to compare energy usage which is an excellent suggestion.

Based on a suggestion I purchased Nevsetpo Power Meter UK Plug Power Monitor Watts Meter Plug and I've used it to test a selection of tasks. Once plugged into a socket it monitors total energy consumption of anything device plugged in. Both machines were fully charged and the "Optimised battery charging" was switched off.

I tried a few computationally intensive tasks and details of energy consumption are here..

 

OpenMM version 7.7.0 has been released

A major focus of this release is on improved force field support. The following have been added.

• GLYCAM is now available for use with Amber14. This is a force field for simulating carbohydrates and glycosylated proteins.
• GBSA implicit solvent is now available for use with Amber14 and CHARMM36. We previously had it for earlier Amber force fields, but not for the more recent ones.
• AMOEBA 2018 is now available, superseding the older 2013 version.

Full details here https://github.com/openmm/openmm/releases.

ARM based Macs are fully supported.

OpenMM is a toolkit for molecular simulation using high performance GPU code

conda install -c conda-forge openmm

 

Pharmacophore searching is a critical part of virtual screening and is can be used to search very large datasets. Pharmacophore query generation in general requires user interaction and is not well-suited for batch mode. Pharmacophore search, on the other hand, can be done in MOE/batch using an SVL script or runnable file that invokes the SVL function ph4_Search. The search was run on a dataset of 274K structures using a predefined query. CCG also provided me with timings from other architectures and the results are shown below. As you can see the MacBook Pro M1 max out-performs all other platforms tested, this is particularly noticeable in the multicore performance, evaluating over 20,000 molecules per second.

You can read the full evaluation of MOE on a MacBook Pro M1max here https://www.macinchem.org/reviews/MacBooks/moe.php

And the list of all applications evaluated here https://www.macinchem.org/reviews/MacBooks/m1macbookpromax.php.

 

Head to head comparison, both cost $899.

Open Babel is now available for osx-arm64 on conda-forge to support Apple Silicon Twitter. Available here https://anaconda.org/conda-forge/openbabel.

Open Babel is a chemical toolbox designed to speak the many languages of chemical data. It's an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas.

 

Everyone's favourite text editor has been updated. BBEdit 14 requires Mac OS X 10.14.2 or later, and is compatible with macOS 10.15 "Catalina" and macOS 11 "Big Sur". Native on Macs with the M1 processor

What's New

Anaconda Virtual Environments - Anaconda is particularly popular with data scientists, as well as with others who need to rapidly switch between different tooling and library configurations. BBEdit 14 will use conda or miniconda to detect your virtual environments, and provides a mechanism for switching the active environment for use when running Unix tools and scripts from within BBEdit.

New Built-In Languages - BBEdit 14 adds built-in syntax coloring and function navigation support for Go, R, Rust, Lisp-family languages (Common Lisp, Scheme, Clojure), and Pixar Universal Scene Description (USD) text files.

Enhanced Developer Features - BBEdit 14 enables several new features and significant improvements to its built-in coding aids for developers, including:

Enhanced language-specific text completions; Improved Find Definition; Assistance for specifying function parameters; New code-navigation features; In-window highlighting of syntax and semantic issues; Language-specific document reformatting. These feature improvements are the result of new built-in support for the Language Server Protocol ("LSP") by which user-installed local "language servers" implement key language-sensitive behaviors. Specific available features may vary by language and by server.

Full details are here.

 

The very popular NMR application iNMR has been updated to version 6.4 in addition iNMR reader has also been updated. It requires Mac OS 11 or higher and it runs on the Apple M1 chip machines.

There are more details here

It is labelled as "Universal" by the Mac Finder. It means that it actually contains two program (it's really fat). One program runs on Intel Macs. The other program runs natively on Macs with the M1 chip. I had also discovered that the previous Mac version worked perfectly with Ma OS 10 but not everything looked the same on Mac OS 11. I have fixed the known issues. Quite likely you'll have had a few minor issues as well. If you are so kind to report them I will happily fix everything during next week. You'll also see a new, corresponding version, of iNMR reader. Another plan was to "notarize" the Mac version. If you are a Mac user you probably know that the Mac, by default, refuses to run iNMR. You are required to right-click the iNMR icon, select "Open" and then authorise iNMR. With the notarization things get simpler. I would like to know: how much is this important for you? I hope you don't care, because for the moment being iNMR is not notarized yet. I have worked mainly on the QuickLook plugin. Please somebody test it. Beware: if you had activated the option "create Thumbnails", then iNMR have surely created many .tiff files that look nice but are not visible in the Finder.

 

Modeller written in Fortran90 has been ported to Apple Silicon

They used the gfortran that's part of the gcc homebrew package (https://brew.sh/). They see about a 20% performance improvement with gfortran-10 on a 2020 Mac Mini (M1) compared to Intel Fortran on a 2018 Mac Mini (Intel).

MODELLER is used for homology or comparative modeling of protein three-dimensional structures. The user provides an alignment of a sequence to be modeled with known related structures and MODELLER automatically calculates a model containing all non-hydrogen atoms. MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints, and can perform many additional tasks, including de novo modeling of loops in protein structures, optimization of various models of protein structure with respect to a flexibly defined objective function, multiple alignment of protein sequences and/or structures, clustering, searching of sequence databases, comparison of protein structures, etc.

If you are using the Homebrew package manager, you can install Modeller on recent Macs (either Intel or Apple Silicon, M1) by simply running

brew tap salilab/salilab
brew install modeller

 

OpenMM is a toolkit for molecular simulation using high performance GPU code.

This is a patch release. It contains a small number of bug fixes, as well as changes to enable two significant additions to the supported platforms.

First, this release provides preliminary support for ARM based Macs. The support has not been extensively tested and should be treated as beta quality. Please try it out and let us know if you encounter any bugs while using it.

Second, we are now providing conda packages for use with Pypy. This is an alternate Python interpreter that uses just-in-time compilation to provide much faster execution than CPython. You can create a conda environment that uses Pypy with the command

conda create -c conda-forge --name pypy pypy

 

Docker is now available for M1 machines

The release note can be found here https://docs.docker.com/docker-for-mac/release-notes/

and you download it here.

https://docs.docker.com/docker-for-mac/install/

 

The very popular bioinformatics tool MacVector 18.1 is now available to download. MacVector 18.1 is a Universal Binary application, which means it runs natively on both Apple Silicon M1 Macs and Intel Macs. MacVector 18.1 matches the “Big Sur” look and feel. …and for the first time in many, many years the MacVector icon has changed to match the square look of macOS Big Sur icons.

We ran some benchmarks to see how much faster MacVector now runs on an Apple Silicon MacBook Pro. We compared this against MacVector 18.0, which runs using Rosetta2 emulation. In some cases you can see that the native Apple Silcon MacVector 18.1 runs 200% faster than the emulated MacVector 18.0.

More on benchmarks here.

 

The full CSD software portfolio, including Mercury, ConQuest, Mogul, GOLD, CSD-CrossMiner, the CSD Python API and other components, has now been upgraded and tested for compatibility with Big Sur. We are pleased to report that the newly available 2020.3.1 CSD Release (only available on macOS) is fully supported on macOS Big Sur at point of release, both for Intel-based macs, as well as the newer M1 Apple silicon based macs. At this point we are aware of just two specific known issues for the newer silicon hardware machines:

• The POV-Ray integration in Mercury for high-resolution graphics generation does not work on M1 Apple silicon based macs
• The Aromatics Analyser component in the CSD-Materials menu of Mercury does not work on M1 Apple silicon based macs We expect that these final remaining issues will be addressed in the next CSD software release.

Full details are here https://www.ccdc.cam.ac.uk/solutions/whats-new/.

More details on scientific applications under Big Sur are here https://www.macinchem.org/blog/files/1fd84c61d3f91608c1b9c413c8064cd4-2692.php

 

We are just starting to see a few benchmarks on the new Apple M1 chip using scientific applications.

This blog post looks like it will be really interesting to follow.

Scientific computing on Apple M1, vol 1: ASE and GPAW.

In this post, which I expect will be the first in a series, I’ll share the code that got me running with a basic Python 3.9, scipy, and matplotlib environment. However, I immediately took it further, getting a working – and quite well-performing – installations of the Atomic Simulation Environment (ASE), used for building, manipulating and visualizing atomistic structure files, as well as a parallel installation of the density functional theory code GPAW.

Bottom line

not even having 10 high-performance Xeon cores in the iMac Pro instead of only 4 high-performance M1 cores in the MacBook Pro brought the two systems to parity: the M1 MacBook Pro handily wins this comparison.

 

Homebrew has been updated

Apple Silicon is now officially supported for installations in /opt/homebrew. formulae.brew.sh formula pages indicate for which platforms bottles (binary packages) are provided and therefore whether they are supported by Homebrew. Homebrew doesn’t (yet) provide bottles for all packages on Apple Silicon that we do on Intel x8664 but we welcome your help in doing so. Rosetta 2 on Apple Silicon still provides support for Intel x8664 in /usr/local.

 

A lot of people have been asking me about running data analysis on the new laptops with M1 chips. It looks like we are starting to see a few benchmarks appearing.

A recent blog post Are The New M1 Macbooks Any Good for Data Science? Let’s Find Out would suggest that the performance of the M1chip continues to impress.

Whilst all benchmarks come with caveats, some use "native" installations others require Rosetta

Python is approximately three times faster when run natively on a new M1 chip, Numpy looks to be slightly slower, Pandas is twice as fast, SciKit-Learn is twice as fast.

Instructions for installing TensorFlow 2.4 on Apple Silicon M1: installation under Conda environment have also been reported.

PyCharm, JetBrains’ IDE for Python development, now supports Apple Silicon M1 processors.

 

The ARM OSX Migration seems to be quite active :-)

Update

And here it is

OpenMM is now available on condaforge for osx-64 to support new Apple hardware based on the Mac M1 chip!

To install

conda install -c conda-forge openmm

I'd be really interested in hearing about any benchmarking activities.

 

A couple of folks have asked me about running Mathematica on Apple Silicon. I don't use Mathematica but the system compatibility is on their website.

Mathematica 12.2 is optimized for the latest operating systems and hardware.

 

An interesting comparison of Apple Intel and M1 chips machines with Nvidia 100 when using Tensorflow.

https://wandb.ai/vanpelt/m1-benchmark/reports/Can-Apple-s-M1-help-you-train-models-faster-cheaper-than-NVIDIA-s-V100---VmlldzozNTkyMzg

We ran a sweep of 8 different configurations of our training script and show that the Apple M1 offers impressive performance within reach of much more expensive and less energy efficient accelerators such as the Nvidia V100 for smaller architectures and datasets.

Code is available on Colab https://colab.research.google.com/drive/1RvZBpzJRW9MNPWQ2rZG8HIyHJbaCwnTI.

They also include tips on setting up a Mac mini to run Tensorflow.

Only initial results on modest data sets, will be interesting to see the performance when Apple releases more Pro hardware.

 

A technical but still very accessible (15 min) analysis of the latest Apple M1 chip. Well worth spending a coffee break viewing.

 

General Issues with Big Sur

If you want an overview of Big Sur I'd recommend the excellent arstechnica review.

Apple has officially confirmed that the following Macs are compatible with Big Sur.

• MacBook (2015 or newer)
• MacBook Air (2013 or newer)
• MacBook Pro (Late 2013 or newer)
• Mac mini (2014 or newer)
• iMac (2014 or newer)
• iMac Pro (2017)
• Mac Pro (2013 and later)

In addition there is issue of the new Apple Silicon Macs with the new Apple M1 chip, if there is specific information about the new machines I've included it.

Snazzy Labs has an interesting commentary on the new Apple Silicon machines and promises a in depth technical review of all 3 models they have on order. The first reviews on Apple Silicon are now in

Scientific Applications

I've contacted all developers I know and their responses to date are shown below.

4-Peaks no known issues

Absoft Absoft Pro Fortran 2021 For macOS and OS X Fully compatible with macOS Big Sur (11.0)

alvaDesc We tested all our products and all of them work properly on MacOS Big Sur.

Amsterdam Modeling Suite As we only just finished our 2020 release our developers didn't dare switching to Big Sur yet. I can try on my own macbook shortly.

Anaconda seems to be working fine.

APE make sure you download the 64-bit version

Avogadro Been running Big Sur betas for a while - everything works. The next beta of Avogadro v2 will have some interface tweaks that are more noticeable on Big Sur (e.g., warning dialogs changed to be Mac-native). Don't yet have universal binaries, but working on it for Open Babel and Avogadro v2 betas (e.g. 1.94). Should still work fine on Apple Silicon computers.

BBEdit version 13 is compatible with macOS Big Sur. BBEdit 13.5 adds support for Apple Silicon

Brainsight macOS 11 Big Sur is coming out very soon, but do not upgrade your Mac yet, because Brainsight is not yet compatible with Big Sur

ChemAxon Most of our software in general requires Java, so as long as the appropriate Java version is installed, there should be no problem.

ChemDraw current version products (ChemDraw Professional 20.0.0.48 and ChemDraw Prime 20.0.0.48) supported with Mac OSX Catalina (v20.0 qualified with Mac OS 11.0 Beta)

ChemDoodle “The latest versions of ChemDoodle 2D (v11) and ChemDoodle 3D (v6) are fully supported on macOS Big Sur (macOS 10.16/11.0) and there are no known issues. In addition to supporting the operating system update, ARM based Macs (like those just released with the new Apple Silicon chip) are also fully supported for ChemDoodle 2D and ChemDoodle 3D with no known issues."

Conquest and Mercury from CCDC The full CSD software portfolio, including Mercury, ConQuest, Mogul, GOLD, CSD-CrossMiner, the CSD Python API and other components, has been tested and is not currently compatible with macOS Big Sur. Please note that our upcoming 2020.3 CSD Release will not be compatible with macOS Big Sur at point of release. We are working hard on a series of improvements to make our software portfolio compatible and fully supported, which we will make available to users as an update as soon as possible. In the meantime, a number of enhancements have been made to our products to simplify the configuration of and support specifically on macOS platforms. Examples are the removal of XQuartz as a dependency and removal of the need to run an X windows display for full use of the CSD Python API. These enhancements will be incorporated in future CSD software releases."

Cresset testing underway

CrystalMaker Works swimmingly! We’ve tested all our software on Big Sur - including a machine with Apple Silicon - and are happy to confirm full compatibility for CrystalMaker 10.5.5, SingleCrystal 4.1.0 and CrystalDiffract 6.8.5. Free updates to CrystalMaker 10.5.5 and SingleCrystal 4.1.0 were released yesterday morning and are recommended for anyone using “Big Sur”.

We are also very-pleased to announce the immediate availability of CrystalMaker 10.6 for Mac, SingleCrystal 4.1 and CrystalDiffract 10.9 for Apple Silicon: these are Universal Binaries and provide 100% native performance on the new Apple Silicon Macs, as well as 100% native performance on Intel.

As one of the vanishingly-few Mac science developers left, we’re proud of our reputation of developing genuine, native Mac apps. We’ve been doing this for over 25 years now and this is our fourth Mac hardware platform! (we’ve gone from Motorola 68K to PowerPC to Intel and now to Apple Silicon).

CYLView 1.0 does not work under Catalina.

DataWarrior Seems to be working fine.

Delta We are working on an update for Delta and will post it as soon as it is available.

DEVONagent fine under Big Sur on Intel, not tested on Apple Silicon.

DEVONthink fine under Big Sur on Intel, not tested on Apple Silicon.

Elemental Fine under Big Sur and on Apple Silicon

EndNote We are in the process of testing and we will be updating our Compatibility page once our testing is complete. Users report no issues on Intel.

EnzymeX no issues reported

EverNote is compatible with Big Sur

Findings no issues reported

Fujitsu ScanSnap .

GAMESS

Gaussaian As far as we can tell so far, both of our current releases, Gaussian 16 rev. C.01 and GaussView 6.1.1 for macOS 64-bit Intel CPUs, run normally on macOS Big Sur. We do not have any reports of problems so far. We do not have a native port for Apple Silicon chips for either G16 or GaussView 6. However, for now, both of the Intel binaries that I mentioned above should run on Apple Silicon using Rosetta 2. We have not done full certification on the Apple Silicon at this point but Apple claims that Rosetta 2 should be capable of handling running existing binaries for Intel chips.

Highlights The PDF Reader for Research, updated for Big Sur with version 2020.3. Big Sur also fixes an annoying issue from PDFKit on Catalina where superscript text (e.g. citations) would break text selection.

Homebrew For most seems to work fine, remember to reinstall Xcode command line tools.

All the ICM products are compatible with Big Sur:

• ICM-Browser
• ICM-Browser-Pro
• ICM-Pro
• ICM-Homology
• ICM-Chemist and ICM-Chemist Pro
• ICM-VLS

Other products that are compatible include:

• MolCart Giga Search (Billion chemical db search)
• MolScreen
• ICM-Scarab

IDL

Igor Pro Igor Pro 8.04 is the first version of Igor that is notarized and includes notarized WaveMetrics XOPs. We therefore recommend that you run Igor Pro 8.04 if you are using macOS 10.15 or macOS 11.0.

iNMR

JalView Jalview works on macOS 11.0 Big Sur!

KaleidaGraph

KNIME We are testing the upcoming version 4.3 of KNIME Analytics Platform against Big Sur and plan to have it supported by the time we do that release in the first week of December.

Manuscripts no issues reported

Matlab MATLAB is compatible with macOS 11 (Big Sur) from release R2020b onwards, support for Apple Silicon is in development

Mathematica no issues for latest version. system requirements

Mendeley no issues reported

Microsoft Office Microsoft has announced that Office 365 is ready for Big Sur - Apple Silicon M1 Macs will use Rosetta 2 for now

Mnova The current version of Mnova LiteCDE is compatible with MacOS Big Sur

NAG Fortran compiler Fortran compiler for Apple Silicon Macs now available to download

MOE Our initial tests showed no issues with this version.

MOPAC

ODYSSEY Molecular Explorer version 6 looks good.

Papers no issues reported

Parallels Parallels 16 is ready for Big Sur. Older Parallels Desktop versions only partially support working on macOS Big Sur due to technical reasons may experience issues depending on the configuration.

PGOPHER

pro Fit pro Fit 7 is now at version 7.0.18, supporting dark mode, Catalina, and Big Sur.

PYMOL On Intel Macs with macOS 11, PyMOL works fine, no known issues. We have not tested Apple Silicon (M1) yet.

Python Python 2.7 is no longer included - use Python 3 instead Python works fine on Apple Silicon and is "mad fast!".

QMForge 2.4 does not support Big Sur yet

R The front page of a CRAN site has a link ‘Download R for (Mac) OS X’. Click on that, then download the file R-4.0.3.pkg and install it. This runs on macOS 10.13 and later on Intel CPU20 (High Sierra, Mojave, Catalina, Big Sur, …).

RDKit All seems fine (Note: -Python 2.7 is no longer supported - use Python 3 instead)

Samson SAMSON 2020 R3 (the latest release) works on Big Sur.

Schrodinger Upgrading to macOS 11 will cause existing Schrödinger Suite releases to fail to run. We are hard at work to address this incompatibility and expect to extend support to macOS 11 in an upcoming Schrödinger Suite release

SeeSAR now updated to support Big Sur.

Sketch With version 70 release, we’re excited to introduce a UI redesign that fits right in with the new macOS Big Sur design language. It’s the same Sketch you know and love, but with every detail reconsidered — from a full-height sidebar, to a refreshed Inspector, and all-new iconography throughout the app.

Spartan There are a few interface issues, tab highlighting on selection is unreliable, not a showstopper but irritating.

SPSS The current release IBM SPSS Statistics 27.0.1 and the current IBM SPSS Statistics Subscription release build 1.0.0.0.1447 will run on MAC OSX 11 Big Sur in translation mode.

Stardrop We haven't yet completed a full test cycle, but we have not seen any issues and don't anticipate any.

Swiss-PdbViewer Swiss-PdbViewer is a 32 bits application and will * NOT * run.

TensorFlow and TensorFlow Addons This pre-release delivers hardware-accelerated TensorFlow and TensorFlow Addons for macOS 11.0+. Native hardware acceleration is supported on Macs with M1 and Intel-based Macs through Apple’s ML Compute framework.

UCSF Chimera ChimeraX v1.1 does not work on MacOS 11.0 (Big Sur), but this problem has been fixed in v1.1.1 and the daily build.

UCSF ChimeraX works on 10.14, 10.15, and 11.0 (Big Sur).

VMD there interface issues under Big Sur

VMWare In preparation for the next major version of macOS 11.0 Big Sur, VMware has made full use of Apple’s hypervisor and other APIs, removing the need for kernel extensions and supporting macOS 11 as both host and guest.

Vortex Vortex works perfectly on Intel Macs under Big Sur, and on Apple Silicon

WINE

Wizard In general works fine, some issue with older work books.

wxMacMolPlt

Xcode need to update to latest version. Remember to reinstall command line tools

XQuartz OpenGL and OpenCL are still here, even on Apple Silicon Macs

I’ll add more updates later, feel free to contact me and thanks for the comments to date.

Last update 17 January 2021