BioArt专·精系列培训 | AI蛋白设计百科全书--跟着三十篇CNS文章系统学习AI蛋白设计——2026年8月18日开课
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Merged summary
TL;DR - BioArt is promoting a 31-lesson training course on AI protein design, scheduled to begin August 18, 2026. The curriculum aims to move learners from interpreting protein models to applying and reproducing AI workflows for protein generation, engineering, and evaluation.
- Eight modules cover protein and Transformer fundamentals, protein language models, inverse folding, de novo generation, structure prediction, binder design, stability optimization, and benchmarking.
- Featured methods include ESM-2, SaProt, RFdiffusion, Chroma, LigandMPNN, Boltz-1, AlphaFold2-Multimer, ProteinGym, and ProteinBench.
- Practical tasks include sequence and structure analysis, mutation-effect prediction, sequence–structure co-design, binder generation, confidence assessment, and candidate mutation screening.
- The course offers CPU and GPU implementations, with most lessons designed to run on CPUs to lower the computational barrier to entry.
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BioArt专·精系列培训 | AI蛋白设计百科全书--跟着三十篇CNS文章系统学习AI蛋白设计——2026年8月18日开课
Public signals
N/A
TL;DR - BioArt is promoting a 31-lesson training course on AI protein design, scheduled to begin August 18, 2026. The curriculum aims to move learners from interpreting protein models to applying and reproducing AI workflows for protein generation, engineering, and evaluation.
- Eight modules cover protein and Transformer fundamentals, protein language models, inverse folding, de novo generation, structure prediction, binder design, stability optimization, and benchmarking.
- Featured methods include ESM-2, SaProt, RFdiffusion, Chroma, LigandMPNN, Boltz-1, AlphaFold2-Multimer, ProteinGym, and ProteinBench.
- Practical tasks include sequence and structure analysis, mutation-effect prediction, sequence–structure co-design, binder generation, confidence assessment, and candidate mutation screening.
- The course offers CPU and GPU implementations, with most lessons designed to run on CPUs to lower the computational barrier to entry.