| RoseTTAFold (GitHub) |
| 蛋白质结构预测新工具--RoseTTAFold问世,比前辈AlphaFold2更快更准确 |
| 一天之内,两大AI预测蛋白结构算法开源,分别登上Nature、Science |
| 准备好迎接蛋白设计的时代了吗?--RoseTTAFold实操 |
| 准备好迎接蛋白设计的时代了吗?--AlphaFold2的两种使用方式 |
| Colab上的AF2:https://github.com/sokrypton/ColabFold |
| 非docker下的AF2:https://github.com/deepmind/alphafold/issues/24 |
AlphaFold2很多不同的玩法:
具体参看:
https://github.com/sokrypton/ColabFold
https://github.com/deepmind/alphafold/issues/24
Gitee国内地址:
https://gitee.com/zerodesigner/easy_af2 |
| Nat. Comput. Sci.|打通蛋白结构预测最后一公里:深度图神经网络有效加速蛋白模型优化 |
| 让子弹飞 | 院士深度解析Alphafold DB的未来影响 |
| Nature | AlphaFold预测98.5%人类蛋白结构,科学研究新范式 |
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| Staem5: A novel computational approach for accurate prediction of m5C site. |
| DeepM6A_cnn ( im6A-TS-CNN) : . im6A-TS-CNN: Identifying the N-6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network |
| DLF-Sul : a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins |
| BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches |
| ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning |
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