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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 seventh edition of Vecura Biotech Insiders, we highlight a workflow shared with the Vecura team by Azlan Firdaus Iskandar: a constrained generative design campaign against amoxicillin's beta-lactam core, and what it took to stop the highest-scoring edit from being the one that breaks the drug.
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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.
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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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Vecura now runs Selenium, the retrosynthesis model from b12 Labs, as the final step of its discovery workflow, so a molecule you generate comes back with routes for making it. Here is what that looked like on a real run against NEK2.
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SuperWater, a generative AI framework developed to predict water molecule positions around biomolecular complexes, offers a new approach to this longstanding computational challenge.
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Hepatocellular carcinoma (HCC) is the predominant primary liver cancer and a major cause of cancer-related mortality worldwide. Despite advances in current therapies, advanced HCC remains difficult to treat, highlighting the need for new therapeutic targets.
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For Biotech Insiders #08, Goh brought us a campaign that did not work, and a reasonable person reading the metrics at stage three would have called it finished. That is exactly why we asked to publish it
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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
