MAP

The official codes for "MAP: A Knowledge-driven Framework for Predicting Single-cell Responses for Unprofiled Drugs".

BioRxiv

We present MAP, a framework that integrates structured pharmacological knowledge into cellular response prediction. MAP learns mechanism-aware drug and gene representations by aligning molecular structures, protein targets, and mechanistic descriptions in a unified embedding space, and then conditions a perturbation predictor on these knowledge-informed representations.

Setup

Environment

We provide requirements.txt as a reference, the versions of packages are not compulsory. The typical installation time for setting up the environment is a few minutes.

Data Preparation

• For knowledge encoders pre-training, we provide preprocessed knowledge graph data files at Huggingface. Download and put them under MAP-KG/data/selected_csvs/.
• For MAP training, download Tahoe-100M, OP3 or SciPlex3 from official sites, and go through all scripts under preprocess/ by alphabetical order.
• We suggest you prepare at least 4 TB storage for the above three datasets.

Implementation

To pre-train knowledge encoders

After environment setup and data preparation, you should first check all the files, and replace all 'path/to/sth' into your own paths, then run:

MAP-KG/train_resume.sh

Training logs and checkpoints will be placed under MAP-KG/logs and MAP-KG/checkpoints.

To train MAP

After environment setup and data preparation, you should first check all the files, and replace all 'path/to/sth' into your own paths, then run:

MAP/train.sh

Training logs and checkpoints will be placed under MAP/logs and MAP/checkpoints.

Demo

We provide a demo to help you understand the expected actions of the model. Run it like this:

python demo.py
  --ckpt [ckpt path]
  --cell_line CVCL_0023
  --drug_smiles "CC1=NC=C(C(=C1O)CO"
  --drug_conc 0.5 --output_dir ./demo_output

Citation

@article{feng2026map,
  title={MAP: A Knowledge-driven Framework for Predicting Single-cell Responses for Unprofiled Drugs},
  author={Feng, Jinghao and Zhao, Ziheng and Zhang, Xiaoman and Liu, Mingfei and Chen, Jingyi and Quan, Xingran and Zhang, Jian and Wang, Yanfeng and Zhang, Ya and Xie, Weidi},
  journal={bioRxiv},
  pages={2026--02},
  year={2026},
  publisher={Cold Spring Harbor Laboratory}
}