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Right into the Saddle: Stereochemistry-Aware Generation of Molecular Transition States

Filipp Nikitin1,2   ·   Dylan M. Anstine3   ·   Olexandr Isayev1,2*
1Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
2Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA
3Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA

📄 Paper   ·   📖 Citation   ·   ⚙️ Setup   ·   🔗 GitHub

*Corresponding author: olexandr@olexandrisayev.com

Overview

Reactions

Abstract

Traditional strategies for calculating molecular transition states are laborious and computationally intensive, thus bottlenecking the scale of reaction modeling. To overcome this limitation, we introduce a flow-matching model, RitS, that directly generates high-quality transition states using only the connectivity of reactants and products as input. Leveraging a newly constructed dataset of approximately 1.7 million transition states, we substantially expand the applicability of previous approaches, which are typically trained on the Transition1x dataset, to cover CHNOSFP elements, larger molecular systems, and both neutral and charged reactions. On the Transition1x benchmark, our model achieves superior performance compared to existing generative approaches. Importantly, RitS enables stereochemistry-aware transition-state generation. We demonstrate selective generation of endo/exo Diels-Alder transition states as well as stereodefined E/Z transition states for chlorostyrene halogenation reactions. Furthermore, the model generalizes to complex multistep organocatalytic reactions, including the classical Hajos-Parrish-Eder-Sauer-Wiechert reaction. Overall, this work represents an important step toward scalable, automated, and high-throughput reaction mechanism analysis that can continue to improve as larger mechanistic datasets become available.

Key Features

  • Direct transition-state generationGenerates 3D transition-state geometries directly from reactant and product connectivity without requiring hand-crafted initial guesses.
  • Stereochemistry-aware generationSupports explicit stereochemical control, enabling generation of transition states corresponding to competing pathways such as endo/exo and E/Z configurations.
  • Flow-matching generative modelUses a flow-matching objective with Kabsch-aligned optimal transport for stable and efficient molecular geometry generation.
  • Large-scale training dataset Trained on a dataset of ~1.7 million transition-state reactions constructed through mechanistic enumeration and active learning.

Setup

Installation will usually take up to 20 minutes.

System and Hardware Requirements

  • OS tested by authors:
    • Ubuntu 24.04 LTS (latest stable Ubuntu LTS at time of writing)
  • Other platforms:
  • Tested inference hardware:
    • GPU: NVIDIA RTX 3090 (24 GB VRAM)
    • CPU: AMD Ryzen 9 5950X
  • Recommended GPU memory:
    • 16-24 GB VRAM for comfortable inference/evaluation with larger molecules and higher batch sizes
  • Minimum practical GPU memory:
    • 8 GB VRAM can run inference, but requires reduced batch sizes
  • CPU-only:
    • Possible, but not recommended and not systematically studied by the authors

OOM mitigation for larger molecules:

  • reduce inference batch size (--batch_size in sampling, or data.inference_batch_size in config)

Prerequisites

  • Python 3.10+
  • CUDA-compatible GPU (recommended for training)
  • Conda or Mamba (recommended)

Environment Setup

# Clone the repository
git clone https://github.com/isayevlab/rits.git
cd RitS

# Create and activate conda environment
conda create -n rits python=3.10 -y
conda activate rits

# Install dependencies
pip install -r requirements.txt
pip install -e .

If you prefer a fully conda-based setup (recommended for RDKit), you can install RDKit via conda-forge before running pip install -r requirements.txt.

The pinned dependencies include h5py, numcodecs, and the Zarr v2 API used by the HDF5 preprocessing pipeline.

For the interactive Streamlit app and IRC post-processing, also install:

pip install -r app/requirements.txt
conda install -c conda-forge xtb

Data Setup

Release folder: Google Drive

Available now:

  • Transition1x training data
  • RGD1-ChEMBL-extended GFN2-xTB training data (data/rgd1_chembl_extended.h5)
  • Transition1x checkpoint (data/ts1x_rits.ckpt)
  • RitS checkpoint trained on RGD1-ChEMBL-extended (data/rits.ckpt)

Expected local layout after downloading and preprocessing both datasets:

data/
├── rits.ckpt
├── ts1x_rits.ckpt
├── rgd1_chembl_extended.h5
├── rits_dataset/
│   └── processed/
│       ├── train_h.pt
│       ├── val_h.pt
│       └── test_h.pt
├── Transition1x/
│   └── wb97xd3/
│       ├── feat_dict.pkl
│       └── raw_data/
│           ├── wb97xd3_ts.xyz
│           └── wb97xd3_fwd_rev_chemprop.csv
└── ts1x/
    └── processed/
        ├── train_h.pt
        ├── val_h.pt
        └── test_h.pt

The downloaded files are the checkpoints, rgd1_chembl_extended.h5, and the Transition1x source files. The rits_dataset/processed/ and ts1x/processed/ directories are generated by the following preprocessing commands.

RGD1-ChEMBL-extended

Prepare the large dataset used by RitS:

python data_processing/prepare_rgd1_chembl_extended.py \
    --h5_file data/rgd1_chembl_extended.h5 \
    --save_data_folder data/rits_dataset

This stage creates random 96%/2%/2% train/validation/test splits. By default it kekulizes aromatic systems into explicit single/double bonds and adds the model's stereochemical pseudo-edges. Training stereochemistry is assigned from the reactant and product 3D coordinates in the HDF5 file. During inference, the same edge encoding is constructed from stereochemistry specified in the input SMARTS. These defaults match the released rits.ckpt; they can be disabled for experiments with --no-kekulize or --no-add-stereo.

Transition1x

Preprocess Transition1x for training:

python data_processing/prepare_ts1x_for_training.py \
    --ts_data data/Transition1x/wb97xd3/raw_data/wb97xd3_ts.xyz \
    --rxn_smarts_file data/Transition1x/wb97xd3/raw_data/wb97xd3_fwd_rev_chemprop.csv \
    --save_dir data/ts1x

Usage

Lightweight runnable examples are available in examples/, with one folder per reaction family (reaction scheme image(s) + cmd.sh).

Model Training

# Train RitS from scratch on preprocessed RGD1-ChEMBL-extended
python scripts/train.py \
    --config-path=conf \
    --config-name rits \
    train.gpus=1 \
    train.seed=28 \
    run_name=test_train \
    outdir="../test_runs" \
    data.dataset_root="./data/rits_dataset"

# Train the Transition1x model instead
python scripts/train.py \
    --config-path=conf \
    --config-name ts1x_rits \
    train.gpus=1 \
    train.seed=28 \
    run_name=ts1x_train \
    outdir="../test_runs" \
    data.dataset_root="./data/ts1x"

# Resume training from checkpoint
python scripts/train.py \
    --config-path=conf \
    --config-name rits \
    train.gpus=1 \
    train.seed=28 \
    run_name=test_train \
    outdir="../test_runs" \
    resume="./data/rits.ckpt"

# Customize training parameters
python scripts/train.py \
    --config-path=conf \
    --config-name rits \
    outdir=./outputs \
    train.gpus=2 \
    train.n_epochs=800 \
    train.seed=42 \
    data.batch_size=150 \
    optimizer.lr=0.0001

Model Inference and Sampling

Transition States Generation

# Generate transition states from atom-mapped SMILES
python scripts/sample_transition_state.py \
    --reactant_smi "[C:1]([c:2]1[n:3][o:4][n:5][n:6]1)([H:7])([H:8])[H:9]" \
    --product_smi "[C:1]1([H:7])([H:8])/[C:2](=[N:3]\\[H:9])[N:6]1[N:5]=[O:4]" \
    --config scripts/conf/rits.yaml \
    --ckpt data/rits.ckpt \
    --output output.xyz \
    --n_samples 1 \
    --batch_size 32

# Generate transition states from XYZ files
python scripts/sample_transition_state.py \
    --reactant_xyz reactant.xyz \
    --product_xyz product.xyz \
    --config scripts/conf/rits.yaml \
    --ckpt data/rits.ckpt \
    --output output.xyz \
    --n_samples 5 \
    --batch_size 32

# Generate multiple samples per reaction
python scripts/sample_transition_state.py \
    --reactant_smi "[C:1][C:2]([H:3])([H:4])[H:5]" \
    --product_smi "[C:1]=[C:2]([H:3])[H:4]" \
    --config scripts/conf/rits.yaml \
    --ckpt data/rits.ckpt \
    --output ts_samples.xyz \
    --n_samples 10 \
    --batch_size 32

Input formats:

  • SMILES: Atom-mapped SMILES with explicit hydrogens (e.g., [C:1][H:2])
  • XYZ: Standard XYZ coordinate files (bonds will be inferred using OpenBabel)

Notes:

  • SMILES must have explicit hydrogens and can use atom mapping to specify atom correspondence
  • Reactant and product must have the same number of atoms
  • Output is saved as XYZ file(s) with transition state coordinates

Available Configurations

Transition State Configs:

  • rits.yaml - RitS stereochemistry-aware conformer generation model trained on our dataset
  • ts1x_rits.yaml - RitS configuration for Transition1x training/evaluation

Interactive App

We also provide an interactive Streamlit web application to make it easier to use RitS without writing any code. The app allows you to select from built-in example reactions or enter custom reaction SMARTS, generate transition states, visualize 2D reaction schemes and 3D transition-state geometries, and optionally run IRC post-processing with pysisyphus.

RitS App 
# Install app dependencies (if not already done)
pip install -r app/requirements.txt
conda install -c conda-forge xtb

# Run the app
streamlit run app/app.py

See app/README.md for more details.

Training Configuration

You can easily override configuration parameters:

# Example with custom parameters
python scripts/train.py \
    --config-path=conf \
    --config-name rits \
    outdir=./my_training \
    run_name=my_experiment \
    train.gpus=4 \
    train.n_epochs=500 \
    data.batch_size=64 \
    data.dataset_root="./data/ts1x" \
    wandb_params.mode=online

Citation

RitS preprint citation:

@article{
rits,
author = {Filipp Nikitin  and Dylan M. Anstine  and Olexandr Isayev },
title = {Right into the Saddle: Stereochemistry-Aware Generation of Molecular Transition States},
journal = {ChemRxiv},
volume = {2026},
number = {0406},
pages = {},
year = {2026},
doi = {10.26434/chemrxiv.15001681/v1},
URL = {https://chemrxiv.org/doi/abs/10.26434/chemrxiv.15001681/v1},
eprint = {https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15001681/v1},
}

You may also find useful our previous LoQI paper:

@article{loqi,
author = {Filipp Nikitin  and Dylan M. Anstine  and Roman Zubatyuk  and Saee Gopal Paliwal  and Olexandr Isayev },
title = {Scalable Low-Energy Molecular Conformer Generation with Quantum Mechanical Accuracy},
journal = {ChemRxiv},
volume = {2025},
number = {0820},
pages = {},
year = {2025},
doi = {10.26434/chemrxiv-2025-k4h7v},
URL = {https://chemrxiv.org/doi/abs/10.26434/chemrxiv-2025-k4h7v},
eprint = {https://chemrxiv.org/doi/pdf/10.26434/chemrxiv-2025-k4h7v}}

This work builds upon the Megalodon architecture. If you use the underlying architecture, please also cite:

@article{reidenbach2026applications,
  title={Applications of modular co-design for de novo 3d molecule generation},
  author={Reidenbach, Danny and Nikitin, Filipp and Isayev, Olexandr and Paliwal, Saee Gopal},
  journal={Digital Discovery},
  year={2026},
  publisher={Royal Society of Chemistry}
}

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Right into the Saddle: Stereochemistry-Aware Generation of Molecular Transition States

chemrxiv.org/doi/full/10.26434/chemrxiv.15001681/v1

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