This is a message Adrià posted in our discord channel. Since he doesn't have an account in the GPUGRID forum I open the thread for him.
If you want to be more up to date to the news related to this project and others please join our discord, we are usually more active there:

https://discord.gg/dCMkcafPpX

Hello GPUGRID!

Here Adrià. I'll be recovering the ACEMD3 application again, and sending new jobs of standard MD simulations
(We've been testing it these past weeks to make sure it worked well for both Windows and Linux)

The main goal of these new batch of simulations will be to validate further our capacity to predict the binding mode of ligands using simulations and adaptive sampling methods. Those of you that have been around for some time here might already be familiar with these simulations, such as the Benzamidin-Trypsin system (https://www.pnas.org/doi/abs/10.1073/pnas.1103547108) or the Dopamine D3 receptor with an antagonist ligand (https://www.nature.com/articles/s41598-018-19345-7#Ack1), which we were able to simulate thanks to GPUGRID and all your effort!

Now, we are revisiting this method, which we call in-silico binding assay (ISBA). During drug discovery campaings, it's common that you know of ligands that bind to your target, but you don't know their binding mode, the exact conformation and structure that both the ligand and the protein have when bound. Knowing the binding mode is critical for further development of the molecule into a potent and usable drug.

The most precise way of discovering the binding mode is with crystallization. However, that can take too much time or be directly impossible, depending on the protein. Therefore, we want to optimize and refine ISBA for binding mode prediction, so it can be usable during drug discovery projects. To summarize a bit our objectives, we want to predict binding modes for larger molecules than Benzamidin, with the same precision, but with less simulation time that was needed for the D3 receptor system.

To do so, we'll be using the latest version of adaptive sampling that we developed, AdaptiveBandit (https://pubs.acs.org/doi/abs/10.1021/acs.jctc.0c00205). The objective of these new simulations I'll be sending will be to benchmark AdaptiveBandit in an ISBA scenario, improve the algorithm if required and fine-tune its hyperparameters.

Let me know if there's any issue with the simulations. I'll be sending 100ns trajectories for the most part, divided in two steps.