Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

10/11/2022
by   Ilia Igashov, et al.
5

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/22/2023

Learning Subpocket Prototypes for Generalizable Structure-based Drug Design

Generating molecules with high binding affinities to target proteins (a....
research
08/23/2023

Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models

Ligand-based drug design aims to identify novel drug candidates of simil...
research
04/21/2023

SILVR: Guided Diffusion for Molecule Generation

Computationally generating novel synthetically accessible compounds with...
research
12/08/2020

Molecule Optimization via Fragment-based Generative Models

In drug discovery, molecule optimization is an important step in order t...
research
03/05/2021

Learning to Extend Molecular Scaffolds with Structural Motifs

Recent advancements in deep learning-based modeling of molecules promise...
research
03/07/2019

Interpretable Deep Learning in Drug Discovery

Without any means of interpretation, neural networks that predict molecu...
research
08/23/2021

C5T5: Controllable Generation of Organic Molecules with Transformers

Methods for designing organic materials with desired properties have hig...

Please sign up or login with your details

Forgot password? Click here to reset