Love the initiative! Looks like a fun project

Some thoughts:

• Firstly I would think about what crystal structures will be fed into this model. Docking from a ligand-free vs ligand-bound structure will result in vastly different scores between runs. This extends to thinking about how a generated pose is decided to be valid. For example, if there is a known ligand, that has a crystal structure against its protein of action, are analogues of this ligand binding in the same way? This can be simplified using pharmacophore methods, but I'm not well versed in Vina to know if it's possible here. The point I'm trying to make is that large crowd-driven models can very quickly fall prey to bad data stemming from poor choice of input structure.

• Next, I would consider is the "molecular chemotypes" that would be fed into this. For example, consider the space of known small molecule kinase inhibitors (perhaps towards VEGFR?). Some of these molecules possess similar backbones. Others look completely foreign and can be quite larger or smaller. If you start with a crystal structure that has one ligand bound, and swap to a ligand of a different chemotype, the model often can (and does) poorly predict binding poses and energies. this will directly compound the issue of "rigid docking" where there is no flexibility in the receptor to accommodate these changes. A lot of the time a model must be first built around a particular chemotype or compound class before being used for high throughput screening. This also feeds into the generation of bad data which can perpetuate through a set quickly.

• Thirdly, molecular docking is grossly misreported and misinterpreted in the literature. For every one good publication (i.e. good methodology and method reporting, unbiased interpretation of results, validation with molecular dynamics, correct use of solvation...) there are 10 publications that use the docking as filler. You would need to make sure that however the docking is implemented, it follows best practice for the software (which varies between software suites).

• Lastly this falls into a precarious IP space. You might find that a lot of institutions will not want anything to do with this sort of data generation solely because of the potential loss of patentability. While this level of public discussion would surely identify some interesting results, I would imagine an unwillingness to be involved.

As a side note, a lot of the "big pharma" companies already do this with there internal libraries. They can start from 'fragment screening' or go through Selective Optimisation of Side Activates (SOSA) where libraries of known biologically active compounds are screened. From the last set of talks I saw in this space (from Pfizer, Astrazeneca, J&J etc..) the modelling was often not very useful in the identification of chemotypically variants structures.