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Official implementation of "Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design"

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# SQUID :squid:

This repository contains the necessary scripts to train and evaluate the 3D generative model SQUID from the paper:

## Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design

Paper Link: https://arxiv.org/abs/2210.04893

**Update Dec. 6, 2024:** If you're interested in using SQUID for shape-conditioned 3D molecular generation, I highly recommend you look at our new work on *ShEPhERD* ([Github page](https://github.com/coleygroup/shepherd)), which also enables shape-conditioned molecular design. *ShEPhERD* is far more general than SQUID, and is easier to use, train, and adapt. We also compared *ShEPhERD* and SQUID head-to-head and found *ShEPhERD* to be much better at shape-conditioned 3D molecular generation. However, *ShEPhERD* is much slower at inference than SQUID, and does not guarantee the validity of generated molecules.

*This video demonstrates how SQUID can generate chemically diverse molecules for arbitrary molecular shapes. Specifically, we took a large (>25 heavy atoms) molecule from our test set and used RDKit to generate multiple diverse conformations for this flexible drug-like molecule. For each conformation, we used SQUID to encode the conformer's shape, and then sampled up to 50 novel molecules from the prior. We display the most shape-similar generated molecule (in red), overlaid on the target shape (in blue). The generated conformers have not been post-processed or aligned; their 3D conformations and poses are those directly generated by SQUID.*

**Downloading Data**

Before running any scripts, please download the necessary data (>50 GB when unzipped) from:

https://figshare.com/s/3d2f8fd57d9a65fe237e

This data includes:

- The original train/test sets taken from Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
- Our fragment library, which is extracted from the training set (100 ring-containing fragments and 24 unique atom types)
- Our filtered train/val/test splits, which remove molecules with fragments not included in our fragment library
- The RDKit-generated 3D conformers for all data splits, including the test-set molecules used in the experiments. We include the conformers both before and after we fix acyclic bond distances/angles. 3D molecules with fixed bonding geometries are typically denoted as "artificial" molecules.
- The pre-processed training data required to (re-)train both the graph generator and the rotatable bond scorer.

You can find the scripts used to generate all these data, along with further instructions, in the directory ```dataset_generation/```

The downloaded data also includes the SQUID-generated 3D molecules for our _Shape-Conditioned Generation of Chemically Diverse Molecules_ experiment, along with the (encoded) target molecules. We include these encoded/decoded molecules for user convenience, as re-running this experiment requires access to an (academic) OpenEye license.

**Dependencies**

You will also need to create a new Python (conda) environment with the dependencies listed in ```environment.yml```. The core dependencies needed to run the scripts and notebooks in this repository are as follows:

- notebook (6.4.11) (for running the Jupter Notebook demonstration)
- python (3.10.4)
- cudatoolkit (11.3.1)
- networkx (2.7.1)
- numpy (1.22.3)
- torch (1.11.0) with (cuda 11.3)
- torch-geometric (2.0.4)
- torch-scatter (2.0.9)
- torch-sparse (0.6.13)
- torchaudio (0.11.0)
- torchvision (0.12.0)
- pandas (1.4.2)
- scipy (1.8.1)
- tqdm(4.64.0)
- rdkit (2022.03.2)
- openeye-toolkits (2022.1.1)

To use the openeye toolkits (for optimally aligning 3D molecules with ROCS and computing _aligned_ shape similarities), you will need access to an OpenEye license. Please see https://www.eyesopen.com/academic-licensing for details. After alignment, we always compute and report shape similarity using our own implementation of the (Gaussian-based) Shape Tanimoto (Eq. 1 in our paper). Hence, any other 3D shape-alignment program (besides ROCS) could be substituted if you cannot obtain access to OpenEye. One option is Shaep (https://users.abo.fi/mivainio/shaep/index.php), which is free to use but is significantly slower than ROCS.

**Directory Organization**

This directory is organized as follows:

- ```dataset_generation/``` contains scripts to process user-provided csv files of SMILES strings into 3D conformers (for evaluations) and training data
- ```models/``` contains the implementation of SQUID
- ```utils/``` contains support functions used across training and generation
- ```trained_models/``` contains trained models for the graph-generator and the rotatable bond scorer
- ```MO_virtual_screening/``` contains scripts used for virtual screening (VS) in our _Shape-Constrained Molecular Optimization_ experiment

- ```train_graph_generator.py``` and ```train_scorer.py``` contain scripts to train the graph generator and the scorer, respectively.
- ```shape_conditioned_generation_evaluations.py``` contains the script to evaluate SQUID in our _Shape-Conditioned Generation of Chemically Diverse Molecules_ experiment.
- ```shape_conditioned_generation_dataset_baseline.py``` contains the script to compute the dataset baseline for our S_hape-Conditioned Generation of Chemically Diverse Molecules_ experiment.
- ```shape_constrained_optimization_evaluations.py``` contains the script to run the genetic algorithm for our _Shape-Constrained Molecular Optimization_ experiment.

**Notebook Demonstration**

```RUN_ME.ipynb``` provides a lightweight, interactive demonstration of how we can easily use SQUID to generate chemically diverse molecular analogues with high shape similarity to an encoded molecule.

**(Re)-Training**

After downloading the training data, you can train the graph generator and the scorer (with their default settings, on 1 gpu) by running:

`python train_graph_generator.py`

`python train_scorer.py`

**Running Experiments**

You can re-generate the SQUID-generated molecules analyzed in our _Shape-Conditioned Generation of Chemically Diverse Molecules_ experiment by running:

`python shape_conditioned_generation_evaluations.py {experiment_name} {lambda_interp} {stop_threshold}`

For instance, to generate 50 samples for 1000 target molecular shapes in the test set using $\lambda = 1.0$ (e.g., sampling from the prior), as performed in our paper, run:

`python shape_conditioned_generation_evaluations.py lambda10 1.0 0.01`

This will create pickle files containing the generated molecules and the (encoded) target molecules. We include these files in ```paper_results/```, which can be downloaded along with the training data (from https://figshare.com/s/3d2f8fd57d9a65fe237e). These generated molecules can then be filtered by tanimoto similarity to the target and sampled to generate the histograms of shape similarity (vs. chemical similarity) in Figure 3 of our paper.

You can run the _Shape-Constrained Molecular Optimization_ experiment by running:

`python shape_constrained_optimization_evaluations.py {experiment_name} {objective} {mol_index}`

The objective is one of `GSK3B`, `JNK3`, `Osimertinib_MPO`, `Sitagliptin_MPO`, `Celecoxib_Rediscovery`, or `Thiothixene_Rediscovery`. The `mol_index` corresponds to the index of a seed molecule in the test set. In our paper, we use different molecules for each objective. In particular, we use:

`GSK3B`: (99300, 142337, 94211, 13059, 138951, 67478, 128739, 70016)

`JNK3`: (2775, 7994, 10770, 108203, 126430, 9126, 128739, 70016)

`Osimertinib_MPO`: (78600, 81366, 46087, 76561, 87747, 91918, 128739, 70016)

`Sitagliptin_MPO`: (118822, 132656, 130062, 113584, 115006, 140953, 128739, 70016)

`Celecoxib_Rediscovery`: (33351, 14473, 101938, 6686, 1200, 69153, 128739, 70016)

`Thiothixene_Rediscovery`: (25628, 25659, 56430, 137033, 48156, 68289, 128739, 70016)

Hence, you can optimize `GSK3B` while restricting the shape similarity to molecule # 99300 from our test set by running:

`python shape_constrained_optimization_evaluations.py GSK3B_99300 GSK3B 99300`

```MO_virtual_screening/``` contains the scripts used for virtual screening (VS) baseline, which includes screening for 1) each objective and 2) shape similarity to each of the seed (target) molecules.

**Notes**

Finally, please note that this repository will be continuously updated to improve the usability of SQUID.

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