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Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Ilia Igashov · Hannes Stärk · Clément Vignac · Victor Garcia Satorras · Pascal Frossard · Max Welling · Michael Bronstein · Bruno Correia
Event URL: https://openreview.net/forum?id=viZ4G1WZxh »

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.

Author Information

Ilia Igashov (EPFL)
Hannes Stärk (MIT)
Hannes Stärk

I am a first-year PhD student at MIT in the CS and AI Laboratory (CSAIL) co-advised by Tommi Jaakkola and Regina Barzilay. I work on geometric deep learning and physics-inspired ML and applications in molecular biology and other physical systems.

Clément Vignac (EPFL)
Victor Garcia Satorras (University of Amsterdam)
Pascal Frossard (EPFL)
Max Welling (Microsoft Research AI4Science / University of Amsterdam)
Michael Bronstein (USI)
Bruno Correia (Ecole Polytechnique Federale de Lausanne)

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