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Leveraging Side Information for Ligand Conformation Generation using Diffusion-Based Approaches by Wu, Jiamin; Cao, He; Yao, Yuan is a scholarly article available to read on EtoBox.

What is Leveraging Side Information for Ligand Conformation Generation using Diffusion-Based Approaches about?

Ligand molecule conformation generation is a critical challenge in drug discovery. Deep learning models have been developed to tackle this problem, particularly through the use of generative models in recent years. However, these models often generate conformations that lack meaningful structure and randomness due to the absence of essential side information. Examples of such side information include the chemical and geometric features of the target protein, ligand-target compound interactions, and ligand chemical properties. Without these constraints, the generated conformations may not be suitable for further selection and design of new drugs. To address this limitation, we propose a novel method for generating ligand conformations that leverage side information and incorporate flexible constraints into standard diffusion models. Drawing inspiration from the concept of message passing, we introduce ligand-target massage passing block, a mechanism that facilitates the exchange of information between target nodes and ligand nodes, thereby incorporating target node features. To capture non-covalent interactions, we introduce ligand-target compound inter and intra edges. To further i

Author

Wu, Jiamin; Cao, He; Yao, Yuan

Published

2023

Language

EN