PolyTamGen Data, Base Language Model, DPO Checkpoint, and Benchmark Results

This record contains release artifacts for PolyTamGen, a sequence-based multi-target molecule generation framework for polypharmacological target-aware molecular design. The record contains four release files: polytamgen_data.tar.gzProcessed training, benchmark, and feature-store data assets used by the released PolyTamGen code. base-llm_NLM-1b.tarBase NatureLM/NLM language-model directory required for real PolyTamGen checkpoint inference. Extract this archive under source/pp2d/models/ so that it materializes source/pp2d/models/base-llm/NLM-1b/. polytamgen-dpo.pthFinal PolyTamGen-DPO checkpoint used for the main benchmark generation. This slim checkpoint omits optimizer/scheduler states and duplicated base-LLM weights; it requires base-llm_NLM-1b.tar. Place it at source/pp2d/models/polytamgen-dpo/polytamgen-dpo.pth. polytamgen_full_results.tar.gzBenchmark results bundle, including molecule-level scored results, raw-generation outputs, supplement materials, figures, and analysis inputs used to reproduce the reported paper metrics. PolyTamGen conditions a NatureLM SMILES generator on sets of protein targets. The released DPO workflow uses ESM3-derived protein features, a Q-Former-style protein adapter, set-wise target interaction, and direct preference optimization with docking/QED preferences. After downloading the companion source repository, materialize the archives under source/pp2d/ and follow the setup, inference, and paper-alignment instructions in the repository README files. DPO inference uses configs/polytamgen_dpo.yaml, models/base-llm/NLM-1b/, and models/polytamgen-dpo/polytamgen-dpo.pth. The record does not include SFT, ablation, adapter-ablation, or exploratory training checkpoints.