Equivariant 3D-Conditional Diffusion Models for Molecular Linker DesignDownload PDF

Published: 21 Oct 2022, Last Modified: 17 Sept 2023AI4Science PosterReaders: Everyone
Keywords: Molecules, Drug Discovery, Molecular Linker Design, Equivariant, Diffusion Models
TL;DR: We propose a conditional diffusion model for generating molecular linkers between disconnected fragments in 3D
Abstract: 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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