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Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

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arxiv 2210.05274 v1 pith:UIOMX4JA submitted 2022-10-11 cs.LG q-bio.BM

Ilia Igashov , Hannes Stärk , Clément Vignac , Victor Garcia Satorras , Pascal Frossard , Max Welling , Michael Bronstein , Bruno Correia This is my paper · ORCID
classification cs.LGq-bio.BM
keywords fragmentsmoleculardruglinkermodelatomsd-conditionaldesign
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Fragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fragments of interest to obtain chemically-relevant candidate drug molecules. In this work, we propose DiffLinker, an E(3)-equivariant 3D-conditional diffusion model for molecular linker design. Given a set of disconnected fragments, our model places missing atoms in between and designs a molecule incorporating all the initial fragments. Unlike previous approaches that are only able to connect pairs of molecular fragments, our method can link an arbitrary number of fragments. Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments. We demonstrate that DiffLinker outperforms other methods on the standard datasets generating more diverse and synthetically-accessible molecules. Besides, we experimentally test our method in real-world applications, showing that it can successfully generate valid linkers conditioned on target protein pockets.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Atomic Design Transformer: Scaffold-Conditioned 3D Molecule Generation via xTB-Reward Reinforcement Learning

    physics.comp-ph 2026-07 conditional novelty 6.0 of 10

    A plain causal transformer that tokenizes atom positions in local frames generates 3D molecules directly; RL against an xTB relaxation reward lifts topology-preserving valid yield from ~50% to ~95%.

  2. Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation

    cs.LG 2024-12 conditional novelty 6.0 of 10

    An E(3)-equivariant Wasserstein autoencoder with disentangled property and structure latents for controllable 3D molecule generation.

  3. From Graph Diffusion to Graph Classification

    cs.LG 2024-11 conditional novelty 6.0 of 10

    A class-conditioned graph diffusion model trained with a discriminative ELBO loss and permutation majority voting achieves competitive graph classification accuracy.

  4. GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation

    cs.AI 2025-01 conditional novelty 5.0 of 10

    A dual-graph RGCN combined with 3D conditional diffusion reaches 58.9% unknown-class and 67.6% known-class top-1 retrosynthesis accuracy on USPTO-50k, beating prior semi-template methods.

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