MolSculpt: Sculpting 3D Molecular Geometries from Chemical Syntax

• URL: http://arxiv.org/abs/2512.10991v1
• Date: Tue, 09 Dec 2025 06:48:46 GMT
• Title: MolSculpt: Sculpting 3D Molecular Geometries from Chemical Syntax
• Authors: Zhanpeng Chen, Weihao Gao, Shunyu Wang, Yanan Zhu, Hong Meng, Yuexian Zou,
• Abstract summary: MolSculpt is a framework that "sculpts" 3D molecular geometries from chemical syntax.<n> Experiments demonstrate that MolSculpt achieves state-of-the-art (SOTA) performance in textitde novo 3D molecule generation and conditional 3D molecule generation.
• Score: 44.892382469903275
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generating precise 3D molecular geometries is crucial for drug discovery and material science. While prior efforts leverage 1D representations like SELFIES to ensure molecular validity, they fail to fully exploit the rich chemical knowledge entangled within 1D models, leading to a disconnect between 1D syntactic generation and 3D geometric realization. To bridge this gap, we propose MolSculpt, a novel framework that "sculpts" 3D molecular geometries from chemical syntax. MolSculpt is built upon a frozen 1D molecular foundation model and a 3D molecular diffusion model. We introduce a set of learnable queries to extract inherent chemical knowledge from the foundation model, and a trainable projector then injects this cross-modal information into the conditioning space of the diffusion model to guide the 3D geometry generation. In this way, our model deeply integrates 1D latent chemical knowledge into the 3D generation process through end-to-end optimization. Experiments demonstrate that MolSculpt achieves state-of-the-art (SOTA) performance in \textit{de novo} 3D molecule generation and conditional 3D molecule generation, showing superior 3D fidelity and stability on both the GEOM-DRUGS and QM9 datasets. Code is available at https://github.com/SakuraTroyChen/MolSculpt.

Related papers

• NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation [72.22099363325145]
  We propose a foundation model -- NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation.<n>NExT-Mol uses an extensively pretrained molecule LM for 1D molecule generation, and subsequently predicts the generated molecule's 3D conformers.<n>We enhance NExT-Mol's performance by scaling up the LM's model size, refining the diffusion neural architecture, and applying 1D to 3D transfer learning.
  arXiv  Detail & Related papers  (2025-02-18T08:40:13Z)
• MUDiff: Unified Diffusion for Complete Molecule Generation [104.7021929437504]
  We present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates. We propose a novel graph transformer architecture to denoise the diffusion process. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
  arXiv  Detail & Related papers  (2023-04-28T04:25:57Z)
• An Equivariant Generative Framework for Molecular Graph-Structure Co-Design [54.92529253182004]
  We present MolCode, a machine learning-based generative framework for underlineMolecular graph-structure underlineCo-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Our investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design.
  arXiv  Detail & Related papers  (2023-04-12T13:34:22Z)
• Geometry-Complete Diffusion for 3D Molecule Generation and Optimization [3.8366697175402225]
  We introduce the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation. GCDM outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings. We also show that GCDM's geometric features can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules.
  arXiv  Detail & Related papers  (2023-02-08T20:01:51Z)
• Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design [3.032184156362992]
  We introduce a new 3D generative model that enables shape-conditioned 3D molecular design by encoding molecular shape and variationally encoding chemical identity. We evaluate our 3D generative model in tasks relevant to drug design including shape-conditioned generation of chemically diverse molecular structures and shape-constrained molecular property optimization.
  arXiv  Detail & Related papers  (2022-10-06T19:37:34Z)
• Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning [68.8204255655161]
  We introduce a novel framework for scalable 3D design that uses a hierarchical agent to build molecules. In a variety of experiments, we show that our agent, guided only by energy considerations, can efficiently learn to produce molecules with over 100 atoms.
  arXiv  Detail & Related papers  (2022-02-01T18:54:24Z)
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs [79.06686274377009]
  We develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules. We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space. Our method can achieve comparable prediction accuracy but with much smaller computational costs.
  arXiv  Detail & Related papers  (2021-09-30T22:09:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.