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MolPMoFiT

Implementation of Inductive transfer learning for Molecular Activity Prediction: Next-Gen QSAR Models with MolPMoFiT

Molecular Prediction Model Fine-Tuning (MolPMoFiT) is a transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling.

MolPMoFiT is adapted from the ULMFiT using Pytorch and Fastai v1. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with a specific endpoints.

UMSPMFiT Overview

Enviroment

We recommand to build the enviroment with Conda.

conda env create -f molpmofit.yml

Datasets

We provide all the datasets needed to reproduce the experiments in the data folder.

  • data/MSPM contains the dataset to train the general domain molecular structure prediction model.
  • data/QSAR contains the datasets for QSAR tasks.

Experiments

The code is provided as jupyter notebook in the notebooks folder. All the code was developed in a Ubuntu 18.04 workstation with 2 Quadro P4000 GPUs.

  1. 01_MSPM_Pretraining.ipynb: Training the general domain molecular structure prediction model(MSPM).
  2. 02_MSPM_TS_finetuning.ipynb: (1) Fine-tuning the general MSPM on a target dataset to generate a task-specific MSPM model. (2) Fine-tuning the task-specific MSPM to tran a QSAR model.
  3. 03_QSAR_Classifcation.ipynb: Fine-tuning the general domain MSPM to train a classification model.
  4. 04_QSAR_Regression.ipynb: Fine-tuning the general domain MSPM to train a regression model.

Pre-trained Models Download

  1. Download ChEMBL_1M_atom. See notebooks/05_Pretrained_Models.ipynb for instructions of usage.

    • This model is trained on 1M ChEMBL molecules with the atomwise tokenization method (original MoPMoFiT).

  2. Download ChEMBL_1M_SPE. See notebooks/06_SPE_Pretrained_Models.ipynb for instructions of usage.

    • This model is trained on 1M ChEMBL molecules with the SMILES pair encoding tokenization method.
    • SMILES Pair Encoding (SmilesPE) is A Data-Driven Substructure Tokenization Algorithm for Deep Learning.