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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.
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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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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.
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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.
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Vecura Biotech Insiders #01: From Docking to Molecular Dynamics
A community workflow example using AutoDock Vina and GROMACS on Vecura
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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
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Designing a Peptide to Hit LRRK2: A De Novo Generative Pipeline Against the ATP Pocket
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.
Understanding AlphaFold: Revolutionizing Protein Folding
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Understanding AlphaFold: Revolutionizing Protein Folding
AlphaFold, developed by DeepMind, is an AI system that predicts protein structures from amino acid sequences with high accuracy using deep learning, significantly advancing protein folding research. Its impact spans drug discovery, disease research, and synthetic biology, and its open-access nature has fostered global scientific collaboration—making it a landmark example of AI's potential to tackle complex biological challenges.
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Can AI Replace 45 Hours of Manual Pose Inspection? A Covalent Docking Comparison
Covalent inhibitors occupy a unique space in drug discovery. By forming irreversible interactions with target residues, they can achieve sustained target engagement and prolonged pharmacological activity beyond what is often possible with non-covalent ligands. However, optimizing covalent scaffolds presents a distinct challenge: favorable binding affinity alone is insufficient. Productive activity depends on precise geometric alignment between the reactive warhead and the target nucleophile.
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Can We Trust Biomedical AI Agents? Benchmarking Quality, Safety, and Reliability
Biomedical agents are different from general chatbots. They are expected to understand scientific questions, retrieve biomedical knowledge, plan workflows, call computational tools, analyze outputs, and provide safety recommendations. Because of this, evaluating only the final answer is not enough.