Papers › An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

12 Apr 2023arXiv:2304.12436archive 2025-07-28

Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for \emph{de novo} molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for \underline{Mol}ecular graph-structure \underline{Co-de}sign. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including \emph{de novo} molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% Validity) and diverse (98.75% Uniqueness) molecular graphs/structures with desirable properties, but also generate drug-like molecules with high affinity to target proteins (61.8% high-affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

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Jn zaixizhang/molcode/model/features.py official repository ran · honoured contract fingerprinted MIT (permissive) · 4390133ddddf910d · report
Jn_zeros zaixizhang/molcode/model/features.py official repository ran · honoured contract fingerprinted MIT (permissive) · 5a3e9439dc4d5004 · report
coord2diff zaixizhang/molcode/model/egnn.py official repository ran · fixture could not drive it MIT (permissive) · e9fb6b7c8ea39384 · report
unsorted_segment_sum zaixizhang/molcode/model/egnn.py official repository ran · our draft was wrong MIT (permissive) · d96ea1a7adc8e5bb · report
collate_mols zaixizhang/molcode/dataset.py official repository unverified MIT (permissive) · 6866c918f65d3ed4 · report
spherical_bessel_formulas zaixizhang/molcode/model/features.py official repository unverified MIT (permissive) · 1389580b692981df · report

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3D geometryDrug DesignDrug DiscoveryGraph Generation

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