A protein folding robot driven by a self-taught agent

This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemaggluanin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to solve HEs folding episodes. The proposed robotic unfolded structure inhabits a dynamic environment and is driven by a self-taught neural agent. The neural agent can read sensors and control the angles and interactions between individual amino acids. During the training phase, the agent uses reinforcement learning to explore new folding forms that conduce toward more significant rewards. The memory of the agent is implemented with neural networks. These neural networks are noise-balanced trained to satisfy the look for future conditions required by the Bellman equation. In the operating phase, the components merge into a wise up protein folding robot with look-ahead capacities, which consistently solves a section of the HEs protein.

Automated summary

This paper presents a computer simulation of a virtual robot that can learn efficient protein folding policies by itself and solve folding episodes of the Hemaggluanin-Esterase protein from human coronavirus. The robot is driven by a self-taught neural agent and uses reinforcement learning to explore new folding forms. The agent's memory is implemented with neural networks trained to satisfy future conditions required by the Bellman equation.