Event-triggered synchronization and H∞ synchronization of coupled delayed reaction-diffusion memristive neural networks

Lin, Shanrong; Liu, Xiwei; Huang, Yanli

doi:10.22541/au.165656708.82061308/v1

Cited by 1 publication

(1 citation statement)

References 35 publications

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“…GraphBP [ 10 ], AR [ 8 ], Pocket2Mol [ 9 ] and FLAG [ 14 ] generate atoms or motifs in the 3D space of protein pockets using autoregressive approaches with graph neural networks (GNNs). TargetDiff [ 13 ], DiffBP [ 12 ] and DiffSBDD [ 11 ] utilize diffusion models to generate molecules by iteratively denoising them from noise.…”

Section: Introductionmentioning

confidence: 99%

KGDiff: towards explainable target-aware molecule generation with knowledge guidance

Qian,

Huang,

et al. 2023

Briefings in Bioinformatics

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Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance. In this paper, we propose an explainable diffusion model to generate molecules that can be bound to a given protein target with high affinity. Our method explicitly incorporates the chemical knowledge of protein–ligand binding affinity into the diffusion model, and uses the knowledge to guide the denoising process towards the direction of high binding affinity. Specifically, an SE(3)-invariant expert network is developed to fit the Vina scoring functions and jointly trained with the denoising network, while the domain knowledge is distilled and conveyed from Vina functions to the expert network. An effective guidance is proposed on both continuous atom coordinates and discrete atom types by taking advantages of the gradient of the expert network. Experiments on the benchmark CrossDocked2020 demonstrate the superiority of our method. Additionally, an atom-level explanation of the generated molecules is provided, and the connections with the domain knowledge are established.

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Section: Introductionmentioning

confidence: 99%