Molformer

Introduction

This is the repository for our Molformer.

Intsallation

# Install packages
pip install pytorch scikit-learn mendeleev
pip install rdkit-pypi

Dataset

We test our model in three different domains: quantum chemistry, physiology and biophysics. We also provide information of datasets regarding the material science used in the preceding 3D-Transformer. You can download the raw datasets in the following links.

Quantum Chemistry

• QM7 Dataset
  Download (Official Website): http://quantum-machine.org/datasets/
  Download (DeepChem): https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/load_function/qm7_datasets.py#L30-L107
  Discription (DeepChem): https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html#qm7-datasets

• QM8 Dataset
  Download (DeepChem): https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/load_function/qm8_datasets.py
  Discription (DeepChem): https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html?highlight=qm7#qm8-datasets

• QM9 Dataset Download (Official Website): https://ndownloader.figshare.com/files/3195389
  Download (Deep Chem): https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/load_function/qm9_datasets.py
  Download (Atom3D): https://www.atom3d.ai/smp.html
  Download (MPNN Supplement): https://drive.google.com/file/d/0Bzn36Iqm8hZscHFJcVh5aC1mZFU/view?resourcekey=0-86oyPL3e3l2ZTiRpwtPDBg
  Download (Schnet): https://schnetpack.readthedocs.io/en/stable/tutorials/tutorial_02_qm9.html#Loading-the-data

• GEOM-QM9 Dataset
  Download (Official Website): https://doi.org/10.7910/DVN/JNGTDF
  Tutorial of usage: https://github.com/learningmatter-mit/geom/blob/master/tutorials/01_loading_data.ipynb

Physiology

• BBBP
  Download and Description (from Moleculnet): https://moleculenet.org/datasets-1
  Download (from Glambard): https://github.com/GLambard/Molecules_Dataset_Collection
  Description (From Deepchem): https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html
  Description (From DGL-sci): https://lifesci.dgl.ai/api/data.html#tox21

• ClinTox
  Download and Description (from Moleculnet): https://moleculenet.org/datasets-1
  Download (from Glambard): https://github.com/GLambard/Molecules_Dataset_Collection
  Description (From Deepchem): https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html
  Description (From DGL-sci): https://lifesci.dgl.ai/api/data.html#tox21

Biophysics

• PDBbind
  Atom3d: https://github.com/drorlab/atom3d

  1. install atom3D pip install atom3d
  2. download 'split-by-sequence-identity-30' dataset from https://www.atom3d.ai/
  3. preprocess the data by running python pdbbind/dataloader_pdb.py

• BACE
  Download and Description (from Moleculnet): https://moleculenet.org/datasets-1
  Download (from Glambard): https://github.com/GLambard/Molecules_Dataset_Collection
  Description (From Deepchem): https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html
  Description (From DGL-sci): https://lifesci.dgl.ai/api/data.html#tox21

Material Science

• COREMOF
  Download (Baidu Drive): https://pan.baidu.com/s/12N8gM8_TQ1mpBGx6gdkAog (password：l41s)
  Reproduction of PointNet++: python coremof/reproduce/main_pn_coremof.py
  Reproduction of MPNN: python coremof/reproduce/main_mpnn_coremof.py
  Repredoction of SchNet:
  1. load COREMOF python coremof/reproduce/main_sch_coremof.py
  2. run SchNet spk_run.py train schnet custom ../../coremof.db ./coremof --split 900 100 --property LCD --features 16 --batch_size 20 --cuda
     (Note: official script of Schnet cannot be reproduced successfully due to the memory limitation.)

Models

models/tr_spe: 3D-Transformer with Sinusoidal Position Encoding (SPE)
models/tr_cpe: 3D-Transformer with Convolutional Position Encoding (CPE)
models/tr_msa: 3D-Transformer with Multi-scale Self-attention (MSA)
models/tr_afps: 3D-Transformer with Attentive Farthest Point Sampling (AFPS)
models/tr_full: 3D-Transformer with CPE + MSA + AFPS

Quick Tour

Model Usage

After processing the dataset, it is time to establish the model. Suppose there are N types of atoms, and n downstream multi-tasks. If you only need to predict a single property, set n = 1. For multi-scale self-attenion, a dist_bar is needed to define the different scales of local regions, such as dist_bar=[1, 3, 5]. You can also specify the number of attention heads, the number of encodes, the dimension size, the dropout rate, and etc, There we only adopt the defaults.

>>> import torch 
>>> from model.tr_spe import build_model
 
# initialize the model 
>>> model = build_model(N, n).cuda()

# take a 4-atom molecule for example
>>> x = torch.tensor([[1, 1, 6, 8]]).cuda()
>>> pos = torch.tensor([[[7.356203877, 9.058198382, 3.255188164],
                         [5.990730587, 3.951633382, 9.784664946],
                         [1.048332315, 3.912215133, 9.827313903],
                         [2.492201352, 9.097616820, 3.297837121]]]).cuda()
>>> mask = (x != 0).unsqueeze(1)
>>> out = model(x.long(), mask, pos)

>>> import torch 
>>> from model.tr_msa import build_model
 
# initialize the model 
>>> model = build_model(N, n, dist_bar).cuda()

# take a 4-atom molecule for example
>>> x = torch.tensor([[1, 1, 6, 8]]).cuda()
>>> pos = torch.tensor([[[7.356203877, 9.058198382, 3.255188164],
                         [5.990730587, 3.951633382, 9.784664946],
                         [1.048332315, 3.912215133, 9.827313903],
                         [2.492201352, 9.097616820, 3.297837121]]]).cuda()
>>> mask = (x != 0).unsqueeze(1)
>>> dist = torch.cdist(pos, pos).float()
>>> out = model(x.long(), mask, dist)

Motif Extraction

We reply on RDKit to extract motifs in small molecules. Given the SMILES representation of any molecule, we can manually define the substructures using Smarts.

>>> from rdkit import Chem
>>> mol = Chem.MolFromSmiles(smiles)
>>> pattern = Chem.MolFromSmarts('C(=O)')
>>> mol.HasSubstructMatch(pattern) # check whether the molecule has the motif 'C(=O)'
>>> mol.GetSubstructMatches(pattern) # get atoms that belong to the motif 'C(=O)'