热点文献带您关注AI的最新进展——图书馆前沿文献专题推荐服务（91）

2024-07-05

    在上一期热点文献推荐中，我们为您推荐了通信领域的最新进展，包括具有信息伪装能力的多信道高安全无线通信系统，超快全光二次谐波波前整形，基于超表面的全彩自动聚焦Airy光束发射器用于稳定高速水下无线光通信，一种用于无干扰无线通信的滤波可重构智能表面。
    本期我们为您选取了4篇文献，介绍AI扩散模型的最新进展，包括双重扩散模型实现基于靶标口袋的3D分子生成，利用生成扩散模型合成Lagrangian湍流，用于分子连接设计的等变3D条件扩散模型，具有扩散模型的量子电路综合。

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets
Huang, Lei, etc.
NATURE COMMUNICATIONS, 2024, 15(1)
Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (M^pro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.
阅读原文：https://www.nature.com/articles/s41467-024-46569-1

 

 

Synthetic Lagrangian turbulence by generative diffusion models
Li, T., etc.
NATURE MACHINE INTELLIGENCE, 2024, 6
Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere, oceans and astrophysics. Despite exceptional theoretical, numerical and experimental efforts conducted over the past 30 years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to reproduce most statistical benchmarks across time scales, including the fat-tail distribution for velocity increments, the anomalous power law and the increased intermittency around the dissipative scale. Slight deviations are observed below the dissipative scale, particularly in the acceleration and flatness statistics. Surprisingly, the model exhibits strong generalizability for extreme events, producing events of higher intensity and rarity that still match the realistic statistics. This paves the way for producing synthetic high-quality datasets for pretraining various downstream applications of Lagrangian turbulence.
阅读原文：https://www.nature.com/articles/s42256-024-00810-0

Equivariant 3D-conditional diffusion model for molecular linker design
Igashov, Ilia, etc.
NATURE MACHINE INTELLIGENCE, 2024, 6
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 three-dimensional 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. We experimentally test our method in real-world applications, showing that it can successfully generate valid linkers conditioned on target protein pockets.
阅读原文：https://www.nature.com/articles/s42256-024-00815-9

Quantum circuit synthesis with diffusion models
Fuerrutter, Florian, etc.
NATURE MACHINE INTELLIGENCE, 2024, 6(5)
Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. Here we use generative machine learning models, specifically denoising diffusion models (DMs), to facilitate this transformation. Leveraging text conditioning, we steer the model to produce desired quantum operations within gate-based quantum circuits. Notably, DMs allow to sidestep during training the exponential overhead inherent in the classical simulation of quantum dynamics—a consistent bottleneck in preceding machine learning techniques. We demonstrate the model’s capabilities across two tasks: entanglement generation and unitary compilation. The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device. Given their flexibility and generalization abilities, we envision DMs as pivotal in quantum circuit synthesis, both enhancing practical applications and providing insights into theoretical quantum computation.

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