Title: MolMiner: Towards Controllable, 3D-Aware, Fragment-Based Molecular Design
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: fragment-based autoregressive molecular generation, chemically valid by design, inverse design, order-agnostic rollout, 3D-aware geometry, symmetry-aware attachment, multi-property conditioning (12 properties), geometry-biased transformer, conditional molecular design

Abstract: We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports conditional generation of molecules over twelve properties, enabling flexible control across physicochemical and structural targets. Molecules are built via symmetry-aware fragment attachments, with 3D geometry dynamically updated during generation using forcefields. A probabilistic conditioning mechanism allows users to specify any subset of target properties while sampling the rest. MolMiner achieves calibrated conditional generation across most properties and offers competitive unconditional performance. We also propose improved benchmarking methods for both unconditional and conditional generation, including distributional comparisons via Wasserstein distance and calibration plots for property control. To our knowledge, this is the first model to unify dynamic geometry, symmetry handling, order-agnostic fragment-based generation, and high-dimensional multi-property conditioning.

TL;DR: MolMiner is a fragment-based autoregressive molecular generator chemically valid by design, order-agnostic, with 3D- and symmetry-aware attachments  enabling conditional molecular design across 12 physicochemical properties