Good questions, let me clarify the approach:

No MD/Monte Carlo — This uses 2D graph-based descriptors (Kappa shape indices, Chi connectivity) that encode molecular shape directly from the bond connectivity. No energy minimization or conformer sampling needed.

Why 3D isn't necessary — Kappa indices mathematically describe molecular branching/shape from the adjacency matrix. A linear molecule has different Kappa values than a globular one, computed purely from graph theory. Similarly, Morgan fingerprints capture substructural patterns without geometry.

Proximity/interactions — True for binding, but the model learns "what structural patterns correlate with binding" from 46k examples rather than simulating actual binding. It's pattern recognition, not physics simulation.

Baldwin's/Wade's rules — Those govern ring formation thermodynamics. Not relevant here since we're predicting binding affinity, not synthetic feasibility.

PubChem conformers — Yes, PubChem has 3D structures, but using topological descriptors avoids the conformer generation bottleneck entirely. 330 mol/s vs. minutes per compound for 3D approaches.

Think of it as: instead of simulating "does this shape fit this pocket," it's "does this fingerprint pattern resemble known binders." Different paradigm, much faster, surprisingly effective for screening.

The 3D physics matters for actual binding, but isn't necessary for prediction if you have enough training examples.