MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
arXiv:2411.06608 · doi:10.1002/aidi.70138
MolMiner is an autoregressive model that builds molecules by attaching fragments in a geometry‑aware, symmetry‑respecting way, while allowing users to control multiple physicochemical and structural properties during generation.
Abstract
We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports high-dimensional conditional control over twelve physicochemical and structural properties from partial specifications, constructs molecules via symmetry-aware fragment attachments, and conditions each generation step on force-field-relaxed three-dimensional geometry of the partial structure. Conditional control emerges without auxiliary property losses. On targeted property windows, conditioning lifts hit rates by up to 5.25x over unconditional generation and 3.5x over the training distribution itself -- overriding the model's intrinsic biases -- at the cost of a small reduction in unconditional distributional fidelity. MolMiner unifies dynamic geometry, symmetry handling, order-agnostic generation, and scalable multi-property conditioning within a single framework.
Accepted manuscript; matches published version