Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

TL;DR

This paper introduces DiffLinker, a novel E(3)-equivariant 3D diffusion model that designs molecular linkers connecting multiple fragments, automatically determining linker size and attachment points, improving diversity and applicability in drug discovery.

Contribution

The paper presents a new 3D-conditional diffusion model capable of linking multiple fragments and automatically determining linker properties, advancing molecular linker design methods.

Findings

Outperforms existing methods on standard datasets.

Generates more diverse and synthetically-accessible molecules.

Successfully applies to real-world protein pocket targeting.

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.