EvoSynth: Enabling Multi-Target Drug Discovery through Latent Evolutionary Optimization and Synthesis-Aware Prioritization

Published at Nature - Communications Chemistry, DOI 10.1038/s42004-026-01945-4

https://www.nature.com/articles/s42004-026-01945-4

1. Download Pretrained Checkpoints

Pretrained EvoSynth checkpoints are publicly available on Zenodo.
They can be downloaded and extracted into the project directory using the following commands:

# Create and move into the checkpoints directory
mkdir -p MolSculptor/checkpoints
cd MolSculptor/checkpoints

# Download pretrained checkpoints from Zenodo
wget https://zenodo.org/records/17351094/files/auto-encoder.zip
wget https://zenodo.org/record/17351094/files/diffusion-transformer.zip

# Extract the checkpoint archives
unzip auto-encoder.zip
unzip diffusion-transformer.zip

Alternatively, the pretrained models can also be downloaded from Hugging Face: https://huggingface.co/HySonLab/EvoSynth

2. Install Required Dependencies

EvoSynth relies on two core modules: MolSculptor and SPARROW. Each module has its own dependency requirements and installation procedure. Before running EvoSynth, please install all required packages by following the detailed instructions provided in:

• MolSculptor/README.md
• SPARROW/README.md

3. Run EvoSynth Inference

Once the checkpoints are downloaded, molecular generation can be performed using the provided shell script:

bash ./MolSculptor/diff_evo_opt_dual.sh

This script runs the dual-target optimization pipeline with the pretrained EvoSynth model. Before execution, users should review and update the parameters to suit their specific use case.

4. Run Synthesis-Aware Prioritization with SPARROW

After molecule generation, the resulting .pkl file (e.g., diffusion_es_opt.pkl) can be processed and evaluated for synthetic feasibility using SPARROW.

Step 1. Prepare Input for SPARROW

First, convert the EvoSynth output to a CSV format compatible with SPARROW using the provided shell script:

bash ./MolSculptor/prepare_sparrow_input.sh

Before running, please review and update the parameters inside prepare_sparrow_input.sh to ensure they match your experiment setup.

Step 2. Run SPARROW Prioritization

Once the input file is prepared, execute SPARROW to perform synthesis-aware candidate prioritization using the provided shell script:

bash ./sparrow/run.sh

Before running, make sure to review and modify the parameters in run.sh to match your setup.

Access Docking Datasets

The docking datasets used for EvoSynth fine-tuning are provided in the data/ directory.

MolSculptor/
└── data/
    ├── case_jnk3-gsk3b_docking_scores.csv
    └── case_parp1-pik3ca_docking_scores.csv

These files provide the affinity labels used to fine-tune and evaluate EvoSynth across both dual-target scenarios.

Acknowledgment

This work builds directly upon two prior open-source frameworks that form the foundation of EvoSynth. If you use this repository, please also cite the following works alongside our paper.

• MolSculptor: A diffusion-evolution framework for multi-site inhibitor design.

@article{li2025molsculptor,
  title={MolSculptor: an adaptive diffusion-evolution framework enabling generative drug design for multi-target affinity and selectivity},
  author={Li, Yanheng and Lin, Xiaohan and Hao, Yize and Zhang, Jun and Wu, Yundong and Gao, Yi Qin},
  year={2025}
}

• SPARROW: An algorithmic framework for synthetic cost-aware decision making in molecular design.

@article{fromer2024algorithmic,
  title={An algorithmic framework for synthetic cost-aware decision making in molecular design},
  author={Fromer, Jenna C and Coley, Connor W},
  journal={Nature Computational Science},
  volume={4},
  number={6},
  pages={440--450},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

Please cite our work:

@article{Nguyen2026,
author={Nguyen, Viet Thanh Duy and Pham, Phuc and Hy, Truong-Son},
title={Enabling multi-target drug discovery through latent evolutionary optimization and synthesis-aware prioritization (EVOSYNTH)},
journal={Communications Chemistry},
year={2026},
month={Feb},
day={16},
abstract={Complex diseases, such as cancer and neurodegeneration, feature interconnected pathways, making single-target therapies ineffective due to pathway redundancy and compensatory mechanisms. Polypharmacy, which combines multiple drugs to target distinct proteins, addresses this but often leads to drug-drug interactions, cumulative toxicity, and complex pharmacokinetics. To overcome these challenges, we introduce EVOSYNTH, a modular framework for multi-target drug discovery that combines latent evolution and synthesis-aware prioritization to generate and prioritize candidates with high translational potential. Latent evolution navigates a chemically and functionally informed latent space to identify candidates with strong predicted affinity across multiple targets. Synthesis-aware prioritization evaluates both retrosynthetic feasibility and the trade-off between synthetic cost and therapeutic reward, enabling practical and efficient candidate selection. Applied to dual inhibition of JNK3 and GSK3$\beta$ in Alzheimer's disease and PI3K and PARP1 in ovarian cancer, EVOSYNTH consistently outperforms baseline generative models, achieving higher predicted affinities, improved scaffold diversity, and lower synthesis costs. These findings highlight EVOSYNTH's ability to integrate target-driven generation with practical synthesizability, establishing a scalable framework for multi-target and polypharmacological drug discovery. Our source code and data to reproduce all experiments are publicly available on GitHub at: https://github.com/HySonLab/EvoSynth.},
issn={2399-3669},
doi={10.1038/s42004-026-01945-4},
url={https://doi.org/10.1038/s42004-026-01945-4}
}