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Title: Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

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.

Paper - https://arxiv.org/abs/2210.05274

Speaker: Ilia Igashov -   / igashov  

Twitter Prudencio:   / tossouprudencio  
Twitter Therence:   / therence_mtl  
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Twitter Valence Discovery:   / valence_ai  

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Chapters:

00:00 - Intro
07:28 - Examples of Structure-Based Drug Design
11:03 - Problem Setup
12:15 - Existing Methods for Linker Design
15:18 - Diffusion Models
18:26 - Forward Diffusion Process
23:31 - Denoising Process
26:45 - Equivariance and 3D Conditioning
32:53 - Equivariant Graph Neural Network
39:37 - DiffLinker - Predicting the # of Atoms in the Linker
42:26 - Results Overview
49:32 - Pocket Conditioning
51:15 - Performance & Conclusion
53:27 - Q+A