mlsanityData to 14 Oct 2026

Decoupled coordinates for machine learning-based molecular fragment linking

arXiv 2111.02930 ↗First posted 1 Nov 2021q-bio.BM cs.LGcited by 1

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Abstract (arXiv)

Recent developments in machine-learning based molecular fragment linking have demonstrated the importance of informing the generation process with structural information specifying the relative orientation of the fragments to be linked. However, such structural information has not yet been provided in the form of a complete relative coordinate system. Mathematical details for a decoupled set of bond lengths, bond angles and torsion angles are elaborated and the coordinate system is demonstrated to be complete. Significant impact on the quality of the generated linkers is demonstrated numerically. The amount of reliable information within the different types of degrees of freedom is investigated. Ablation studies and an information-theoretical analysis are performed. The presented benefits suggest the application of a complete and decoupled relative coordinate system as a standard good practice in linker design.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 47 references that are in the map. First posted on arXiv 1 Nov 2021.

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6 of 47 references.

References · 6

  1. 2013Auto-Encoding Variational Bayescited by 4,053
  2. 2015Gated Graph Sequence Neural Networkscited by 796
  3. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  4. 2017Toward Multimodal Image-to-Image Translationcited by 296
  5. 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 185
  6. 2018Learning Multimodal Graph-to-Graph Translation for Molecular Optimizationcited by 88

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