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Protein structure prediction is a fundamental problem in computationalmolecular biology. Classical algorithms such as ab-initio or threading as wellas many learning methods have been proposed to solve this challenging problem.However, most reinforcement learning methods tend to model the state-actionpairs as discrete objects. In this paper, we develop a reinforcement learning(RL) framework in a continuous setting and based on a stochastic parametrizedHamiltonian version of the Pontryagin maximum principle (PMP) to solve theside-chain packing and protein-folding problem. For special cases ourformulation can be reduced to previous work where the optimal foldingtrajectories are trained using an explicit use of Langevin dynamics. Optimalcontinuous stochastic Hamiltonian dynamics folding pathways can be derived withuse of different models of molecular energetics and force fields. In our RLimplementation we adopt a soft actor-critic methodology however we can replacethis other RL training based on A2C, A3C or PPO.

Solving the Side-Chain Packing Arrangement of Proteins from Reinforcement Learned Stochastic Decision Making