tutorials

molSimplify can be used to visualize molecules built from SMILES strings. In this tutorial, we will use molSimplify to generate several variants of a molecule and display them in a grid.

Install recommendations: For users who intend to develop (i.e., change the source code) with molSimplify, we recommend an install from source. If you have difficulties installing from source, we recommend Docker or Conda, where we maintain a static image of molSimplify that includes machine learning models that we have published.

With the static classifier, we achieve great performance on job status predictions of out-of-sample data points, capturing their failure before doing the simulation thus saving lots of computational resources (Tutorial 12).

Geometry optimization with density functional theory (DFT), a general procedure to obtain the ground state structures of a complex, is computationally demanding in terms of time and can also easily fail.

Many of the classes and methods that are used in molSimplify can be incorporated into your own python scripts. This can be used to extend the geometric manipulation routines in molSimplify to custom use-cases, or to help post-processing the results of DFT calculations.

molSimplify comes with about 160 built in common ligands and a nifty decoration manager to modify them. However, this cannot hope to address the scope of possible ligands, so we also support providing your own ligands as SMILES or in 3D molecule formats.

In this two-part tutorial, we’ll show you how to use molSimplify to collect autocorrelation-based descriptors from molecular structures and use those to make predictions using a simple kernel ridge regression (KRR) model, as shown in our recent paper.

In the second part of our tutorial, we will demonstrate how to use R to conduct kernel based prediction of atomization energies based on RACs. You’ll need QM9_descriptor_file.csv, which we prepared using molSimplify in the previous tutorial and also provide here.

In this tutorial, we will show how to replace multidentate ligands in a prebuilt complex. This feature, which facilitates screening of diverse catalyst scaffolds, was recently added to molSimplify. Today’s example involves the following Zn(II) intermediate in a model carbonic anhydrase catalytic cycle, stored as zncat in our Cores subdirectory:

So far in our molSimplify tutorials, we have been focusing on generating structures for first row transition metal complexes. While the study of such complexes is certainly crucial for research areas such as catalysis and pharmacology, it is imperative that we also include heavy metal complexes in our investigations in order to motivate rapid advancement of inorganic chemistry research.