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来自 Vecura 团队的视角

关于 AI 驱动生命科学探索的研究分析、行业视角与深度解析

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From Prediction to Decision: Choosing Graph-Based AI Models for Drug-Target Interaction Research

How an output-driven perspective can help researchers select graph-based AI models for drug-target interaction research, with a closer look at DTIGN and LigoSPACE.

Dr. Thanh Nguyen, Duy Anh Nguyen +1·Jul 22, 2026阅读精选文章

洞察

Vecura Biotech Insiders #05: Lead Optimization Against NEK2

A User-Shared Workflow from the Vecura Community Lead Optimization Against NEK2: What Three Generative Rounds Cost, and What They Bought

Aug 14, 2026

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Making Water Visible: How Generative AI Can Support Protein Structure Research

SuperWater, a generative AI framework developed to predict water molecule positions around biomolecular complexes, offers a new approach to this longstanding computational challenge.

DR. Yunchao (Lance) Liu·Jul 14, 2026

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Vecura Biotech Insiders #02: De novo Antibody Design Pipelines on Vecura

In this second edition of Vecura Biotech Insiders, we highlight a workflow shared with the Vecura team by Tony 阮进成 during the support process.

Jul 24, 2026

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From Screening Workflow to Shelf: What an ODM/OEM Partner Actually Gets from Vecura

A beauty-from-within case study — collagen-led nutricosmetics

Aug 7, 2026

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Vecura Biotech Insiders #04: Vibe in Silico on Vecura

In this Vecura Biotech Insiders feature, we explore a perspective on where agentic AI actually sits alongside physics-based simulation and learned prediction — and what's left to call "vibe in silico" once you subtract what the agent actually computed.

Aug 6, 2026

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Vecura Biotech Insiders #03: Building a Humanized Tyrosinase Model for Structure-Based Docking

In this Vecura Biotech Insiders feature, we explore a user-shared workflow for building a copper-tagged human tyrosinase model from AlphaFold, benchmarking it against mushroom tyrosinase, and preparing it for structure-based docking.

Jul 30, 2026

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Building the Active Sweet Receptor from a Cloud of Density

A walkthrough of how to build an atomic model from a cryo-EM density map

Jul 24, 2026

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Using Boltz-2.1 for Structure Confidence Screening in a De Novo Protein Design Workflow

How the Vecura platform runs Boltz-2.1 through the API as the structure-validation engine inside a multi-model design pipeline, folding nine designed sequences with ten sampled models each and turning per-structure confidence into a triage signal for what to carry forward.

Jul 16, 2026

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De Novo Design of Sartan-Like AT1R Binders for Companion-Animal Kidney Health

A Vecura workflow, chaining generative small-molecule design, structure-based docking, ADMET prediction and toxicophore-constrained redesign against the angiotensin II type 1 receptor. What the platform surfaced was not a drug candidate but something more useful, a quantified account of the potency-safety tradeoff that governs this chemotype.

Jul 15, 2026

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Vecura Biotech Insiders #01: From Docking to Molecular Dynamics

A community workflow example using AutoDock Vina and GROMACS on Vecura

Jul 10, 2026

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How to Slow down Amyotrophic Lateral Sclerosis - An In-Silico Approach to SOD1 Stabilization

A proposed computational pipeline for identifying small-molecule binders at the SOD1 dimer interface

Jul 3, 2026
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Vecura

产品

  • 解决方案
  • 定价

公司

  • 联系我们
  • 学术研究计划

资源

  • 更新
  • 新闻
  • 洞察
  • 使用案例
  • AI4Life Bootcamp
  • 社区

法律条款

  • 隐私政策
  • 服务条款
  • 信任中心

© 2026 NYB AI 保留所有权利。

所有系统运行正常