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Related Experiment Video

Updated: Dec 25, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Deep Generative Models for 3D Linker Design.

Fergus Imrie¹, Anthony R Bradley², Mihaela van der Schaar^3,4

¹Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, U.K.

Journal of Chemical Information and Modeling

|March 21, 2020

Summary

This summary is machine-generated.

We developed DeLinker, a novel deep generative model for rational compound design. It uniquely uses 3D structural information to design molecules, outperforming existing methods by 60-200%.

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Interactive Molecular Model Assembly with 3D Printing

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

Area of Science:

Computational chemistry
Drug discovery
Machine learning

Background:

Rational compound design is complex for computational methods and chemists.
Current generative models lack 3D structural information utilization.
Three-dimensional structural context is crucial for effective molecular design.

Purpose of the Study:

Introduce DeLinker, a novel graph-based deep generative model.
Integrate 3D structural information into molecular generation.
Enhance compound design for fragment linking, scaffold hopping, and PROTACs.

Main Methods:

Developed a graph-based deep generative model (DeLinker).
Incorporated protein-context-dependent 3D structural information (relative distance and orientation).
Evaluated performance against a database baseline.

Main Results:

DeLinker designed 60% more molecules with high 3D similarity than baseline.
Outperformance increased to 200% for longer linkers (≥5 atoms).
Demonstrated effectiveness in fragment linking, scaffold hopping, and PROTAC design.

Conclusions:

DeLinker is the first molecular generative model to directly use 3D structural information.
Incorporating 3D context significantly improves molecular design outcomes.
The method shows broad applicability across various drug design challenges.