Back in July, some very lucky OPIGlets ventured across the pond to discover life in Southern Maine (and Boston!). For someone visiting Boston for the first time, no trip would be complete without a Red Sox game—a thoroughly enjoyable highlight (see Figure 1). While we were there, we also went to Gordon Research Conference (GRC) on Computer Aided Drug Design (CADD).
TLDR; pip install pymol-open-source
In our recent work with the PoseBusters benchmark, we made a deliberate choice: to include both receptors seen during training and completely novel ones. Why? To explore an often-overlooked question: how much does receptor similarity to training data influence model performance?
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Do you keep downloading .pdb and .sdf files and loading them into PyMol repeatedly?
If yes, then PyMol remote might be just for you. With PyMol remote, you can control a PyMol session running on your laptop from any other machine. For example, from a Jupyter Notebook running on your HPC cluster.
Continue readingIt is good practice not to commit a data folder to version control if the data is available elsewhere and you do not want to track changes of the data. But do not forget to also add an entry for this folder to .gitignore because otherwise git iterates over all the files in the folder when checking for file changes, which may take a long time if there are many files.
Some molecular pose generation methods benefit from an energy relaxation post-processing step.
Here is a quick way to do this using OpenMM via a short script I prepared:
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Deep learning (DL) methods in structural modelling are outcompeting force fields because they overcome the two main limitations to force fields methods – the prohibitively large search space for large systems and the limited accuracy of the description of the physics [4].
However, the two methods are also compatible. DL methods are helping to close the gap between the applications of force fields and ab initio methods [3]. The advantage of DL-based force fields is that the functional form does not have to be specified explicitly and much more accurate. Say goodbye to the 12-6 potential function.
In principle DL-based force fields can be applied anywhere where regular force fields have been applied, for example conformation generation [2]. The flip-side of DL-based methods commonly is poor generalization but it seems that force fields, when properly trained, generalize well. ANI trained on molecules with up to 8 heavy atoms is able to generalize to molecules with up to 54 atoms [1]. Excitingly for my research, ANI-2 [2] can replace UFF or MMFF as the energy minimization step for conformation generation in RDKit [5].
So let’s use Auto3D [2] to generated low energy conformations for the four molecules caffeine, Ibuprofen, an experimental hybrid peptide, and Imatinib:
CN1C=NC2=C1C(=O)N(C(=O)N2C)C CFF CC(C)Cc1ccc(cc1)C(C)C(O)=O IBP Cc1ccccc1CNC(=O)[C@@H]2C(SCN2C(=O)[C@H]([C@H](Cc3ccccc3)NC(=O)c4cccc(c4C)O)O)(C)C JE2 Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CCN(CC5)C STIContinue reading
CASF-2016 is a commonly used benchmark for docking tools. Unfortunately, some of the provided ligand files cannot be loaded using RDKit (version 2022.09.1) but there is an easy remedy.
Continue readingThe Astex Diverse set [1] is a dataset containing the crystallized poses of 85 protein-ligand complexes. It was introduced in 2007 to address problems in previous datasets such as incorrect ligand representation.
Loading the 85 ligand files with today’s version of the cheminformatics toolkit RDKit [2] is, however, not as straightforward as you might expect.
Continue readingThe results are in! 19 group members responded within 24 hours and rated 8 potential swag items on a scale from ⭐ to ⭐⭐⭐⭐⭐. Let’s see what they voted.
First, let’s take a look at the average rating per item. People gave many stars to the coffee mug, socks, notebook, and hat.
