{"data":{"kind":"file","path":"README.md","version_id":"vdvi2cglo4uflhdzaq7wscch","entry":{"name":"README.md","path":"README.md","is_directory":false,"size":1329,"modified_at":"2026-08-11T13:58:59.110000","content_hash":"1cb1f66565077c992028854fbbf2811140ebbeea37d175c25860ec38932fd277"},"entries":[],"content":"# Protein Folding Toy Model / Energy Minimizer\n\nA verifiers environment that evaluates LLM reasoning about simplified protein folding using 2D lattice HP (Hydrophobic-Polar) models.\n\n## Overview\n\nThis environment presents toy protein sequences on a 2D square lattice where:\n- **H** (hydrophobic) residues attract each other when adjacent but not bonded\n- **P** (polar) residues are neutral\n- The energy function counts H-H non-bonded contacts (each contact = -1 energy)\n- The goal is to find conformations that minimize total energy\n\n## Tasks\n\n1. **Energy Calculation**: Given a sequence and a conformation (path on 2D grid), compute the energy correctly\n2. **Conformation Design**: Find low-energy conformations for given sequences\n3. **Algorithm Design**: Describe and reason about optimization strategies (Monte Carlo, simulated annealing, genetic algorithms)\n4. **Analysis**: Identify flaws in proposed conformations or algorithms\n\n## Scoring\n\n- **Energy accuracy**: Correct computation of H-H contacts (0.0 - 1.0)\n- **Conformation validity**: Self-avoiding walk on 2D lattice (0.0 - 1.0)\n- **Optimization quality**: Energy improvement over random (0.0 - 1.0)\n- **Reasoning depth**: Quality of algorithmic reasoning (0.0 - 1.0)\n\n## Usage\n\n```python\nimport verifiers as vf\nenv = vf.load_environment(\"protein-folding-toy\")\n```\n","encoding":"utf-8","truncated":false,"total_bytes":1329},"status":null}