Software Open Access
Major Release (v0.1.0)
Gym-lattice is an HP 2D Lattice Environment with a Gym-like API for the protein folding problem.
This is a Python library that formulates Lau and Dill's (1989) hydrophobic-polar two-dimensional lattice model as a reinforcement learning problem. It follows OpenAI Gym's API, easing integration for reinforcement learning solutions.
render()draws the chain on the command-line.
Additionally, there is an option to set the penalty parameters for training the agent, this includes:
collision_penalty): accounts for the time whenever the agent decides to assign a molecule to an already-occupied space; and
trap_penalty): induces heavy deductions whenever the agent traps itself and cannot accomplish the task anymore.
assertswhen catching errors.
Lau, K.F. and Dill, K.A., 1989. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22(10), pp.3986-3997.