洞察
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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In this sixth edition of Vecura Biotech Insiders, we highlight a workflow shared with the Vecura team by Xiao Jing Chen: a scaffold-based peptide design campaign against a scorpion defensin, and what happened when the folded structures and the sequences behind them were finally read side by side.
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A User-Shared Workflow from the Vecura Community Lead Optimization Against NEK2: What Three Generative Rounds Cost, and What They Bought
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A beauty-from-within case study — collagen-led nutricosmetics
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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.
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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.
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A walkthrough of how to build an atomic model from a cryo-EM density map
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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.
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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.
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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.
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A community workflow example using AutoDock Vina and GROMACS on Vecura
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A proposed computational pipeline for identifying small-molecule binders at the SOD1 dimer interface
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A walkthrough of how modern protein design models behave when pointed at a hard, conserved kinase pocket, and what the output does and does not tell us.