I have similar concerns regarding the ligand sets used to test Alphafold3. I’ve had a cursory look at them and it seemed to me there were a lot phosphate containing molecules, a fair few sugars, and also some biochemical co-factors. I haven’t done a detailed analysis, so some caveats. But if true, there are two points here. Firstly there will be a lot of excellent crystallographic training material available on these essentially biochemical entities, so AlphaFold3 is more likely to get these particular ones right. Secondly, these are not drug-like molecules and docking programs are generally parameterized to dock drug-like molecules correctly, so are likely to have a lower success rate on these structures than on drug-like molecules.
I think a more in-depth analysis of performance of AF3 on the validation data is required, as the OP suggests. The problem here is that biochemical chemical space, which is very well represented by experimental 3D structure, is much smaller than potential drug-like chemical space, which is poorly represented by experimental 3D structure comparatively speaking. So inevitably AF3 will often be operating beyond the zone of applicability, for any new drug series. There are ways of getting round this data restriction, including creating physics compliant hybrid models (and thereby avoiding clashing atoms). I’d be very surprised if such approaches are not currently being pursued.
I have similar concerns regarding the ligand sets used to test Alphafold3. I’ve had a cursory look at them and it seemed to me there were a lot phosphate containing molecules, a fair few sugars, and also some biochemical co-factors. I haven’t done a detailed analysis, so some caveats. But if true, there are two points here. Firstly there will be a lot of excellent crystallographic training material available on these essentially biochemical entities, so AlphaFold3 is more likely to get these particular ones right. Secondly, these are not drug-like molecules and docking programs are generally parameterized to dock drug-like molecules correctly, so are likely to have a lower success rate on these structures than on drug-like molecules.
I think a more in-depth analysis of performance of AF3 on the validation data is required, as the OP suggests. The problem here is that biochemical chemical space, which is very well represented by experimental 3D structure, is much smaller than potential drug-like chemical space, which is poorly represented by experimental 3D structure comparatively speaking. So inevitably AF3 will often be operating beyond the zone of applicability, for any new drug series. There are ways of getting round this data restriction, including creating physics compliant hybrid models (and thereby avoiding clashing atoms). I’d be very surprised if such approaches are not currently being pursued.
I’m surprised by how knowledgeable people are about this on this site!
BTW, there’s some discussion of this happening on the CCL mailing list (limited to professionals in relevant fields) if you are interested.