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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Dan Li

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#protein folding Jun 2025

Unraveling the Efficacy of AR Antagonists Bearing N-(4-(Benzyloxy)phenyl)piperidine-1-sulfonamide Scaffold in Prostate Cancer Therapy by Targeting LBP Mutations.

LT16 outperformed existing antiandrogens by fully antagonizing clinical AR mutations and effectively suppressing castration- and enzalutamide-resistant LNCaP cells proliferation in vitro and in vivo, positioning it as a promising and innovative therapeutic for advanced PCa.

Xin Chai, Xinyue Wang, Lvtao Cai et al. · 2 citations
#large language models Sep 2025

Discovery of N-(thiazol-2-yl) Furanamide Derivatives as Potent Orally Efficacious AR Antagonists with Low BBB Permeability.

Resistance-conferring mutations in the androgen receptor (AR) ligand-binding pocket (LBP) compromise the effectiveness of clinically approved orthosteric AR antagonists. Targeting the dimerization interface pocket (DIP) of AR presents a promising therapeutic approach. In this study, we report the design and optimizatio...

Jin-Biao Liao, J. Liao, Yanzhen Yu et al. · 1 citation
#machine learning Open access Jul 2025

Effective generation of heavy-atom-free triplet photosensitizers containing multiple intersystem crossing mechanisms based on deep learning

This work proposes a novel strategy that incorporates two models: a fragment-based model (Frag-MD) and a character-based model (MD), both integrating a conditional transformer, recurrent neural networks, and reinforcement learning that holds the potential to establish a new paradigm for discovering novel PSs applicable...

Kepeng Chen, Xiaoting Zhang, Ji-Ke Wang et al. · 4 citations
#quantum computing Jun 2026

Overcoming Resistance in the Androgen Receptor: Rational and Strategic Design of Advanced Antagonists.

Advances in elucidating the molecular mechanisms underlying AR conformational regulation are summarized and progress in the structure-based design and development of novel AR antagonists are highlighted, highlighting the power of computation-driven approaches in drug discovery.

Xin Chai, Tingjun Hou, Dan Li · 0 citations
#machine learning Jan 2026

MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDoc...

Hui Zhang, Xujun Zhang, Qun Su et al. · 6 citations
#large language models Jan 2026

Discovery of Novel Nonsteroidal SGRMs of Sulfonamide-2-Oxo-Tetrahydroquinoline Derivatives by Carbonyl Migration.

D8, which contained 2-oxo-tetrahydroquinoline by carbonyl migration form B53, manifests an excellent SGRM with remarkable transrepression potency and optimized binding mode within the GR LBD, underscoring its therapeutic potential and validating the design strategy.

Xiaodong Bao, Yuxin Zhou, Zhaoxu Yang et al. · 1 citation
#machine learning Open access Aug 2026

AI-driven PROTAC design overcomes oncogenic resilience by eliminating the CLIP1-LTK fusion protein.

The AI-guided development of a first-in-class proteolysis-targeting chimera (PROTAC) designed to selectively degrade the CLIP1-LTK fusion protein is reported, providing a promising therapeutic strategy for overcoming acquired resistance in kinase-driven cancers.

Shi-Cheng Chen, Hai-Ting Duan, S. Zhong et al. · 0 citations

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