Data and code for, "Large language models design sequence-defined macromolecules via evolutionary optimization"

# Codes and data for "Large language models design sequence-defined macromolecules via evolutionary optimization"Note this repository contains codes and data files for the manuscript. This is a snapshot of the repository, frozen at the time of submission.# Codes## LLM codes- `run_claude.py` - the routine for performing LLM-based rollouts; intended for command line execution using argparse- `message_utils.py` - utilities for constructing and parsing messages for LLM I/O- `model_utils.py` - lightweight utilities for retrieving formatted predictions from the RNN ensemble- `target_defs.py` - defines the sequence, locations, and natural language descriptions of the target structures- `ask_about_oracle.ipynb` - asks the LLM to speculate about the nature of the optimization task## other algorithms- `active_learning.ipynb` - use EI acquisition with RF surrogate to label new sequences; includes an unused tokenization scheme- `evolutionary_algorithm.ipynb` - use DEAP library to perform evolutionary optimization- `random_sampling.ipynb` - sample sequences randomly from all possible sequences## postprocessing- `process_aggregated_logs.py` - reads data from the raw log files and prepares them for visualization- `process_sample_rollouts.py` - reads data from the raw log files and prepares individual rollouts## visualization- `figure1b.ipynb` - renders panel b of Fig. 1- `figure1efg.ipynb` - renders the last row of Fig. 1 (panels e-g)- `figure2.ipynb` - renders all of Fig. 2- `figure_si.ipynb` - renders Figs. S1 and S2- `figure_md_validation.ipynb` - renders Fig. S3# Data files- `prompts/` - `prompt-scientific-v4.4.yml` - the full text of the scientific prompt, to be read by `run_claude.py` - `prompt-oracle-v4.4.yml` - the full text of the oracle prompt, to be read by `run_claude.py`- `models/` - the TorchScript RNN models used to make predictions- `data/` - `embeddings` - calculated embeddings for a collection of sequences from our prior work - `llm-logs` - the raw logs obtained from the Claude 3.5 Sonnet LLM (other algorithms made to look like the LLM logs after the fact) - `llm-logs-opus` - the raw logs obtained from the Claude 3.0 Opus LLM (used in the first draft of the article, replaced by Claude 3.5 Sonnet) - `all-rollouts-kltd.csv` - postprocessed logs for all the rollouts using the "top $k < d^*$" metric - `all-rollouts-topkd.csv` - postprocessed logs for all the rollouts using the "mean $d$ for top $k$" metric - `sample-rollout-membranes-x-3.csv` - postprocessed logs for a single rollout replica, `x` = each algorithm type - `snapshots` - png snapshots of MD simulation results at different locations in the manifold