In the new paper Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism, a Baidu research team presents a Parallel Evoformer and Branch Parallelism approach for efficient AlphaFold2 training. The novel strategy improves AlphaFold2 training speed by up to 38.67 percent without sacrificing performance.
A research team from the National University of Singapore, HPC-AI Technology Inc., Helixon and Shanghai Jiao Tong University proposes FastFold, a highly efficient protein structure prediction model for training and inference that reduces AlphaFold 2’s training time from 11 days to 67 hours.
In 2016 Google’s DeepMind stunned the world when their Go computer AlphaGo secured a historic victory over Korean grandmaster Lee Sedol. Yesterday the UK’s top AI team delivered their latest “wow moment” as their AI system AlphaFold topped the Critical Assessment of Structure Prediction (CASP) competition.