reading notes of《Artificial Intelligence in Drug Design》

1.Introduction

• These molecular representations are human-made models designed to capture certain properties. The molecules possess the syntactic properties and semantic properties.
• Several factors contribute to the popularity of SMILES in the context of deep learning:
  • SMILES are strings, which renders them suitable as inputs to sequence modeling algorithms.
  • Compared to other string-based molecular representations such as InChI, SMILES strings have a straightforward syntax. This permissive syntax allows for a certain “flexibility of expression”.
  • SMILES are easily legible and interpretable by humans.
• The tool example demonstrates how deep learning methods can be employed to generate sets of new SMILES strings, inspired by the structures of four known retinoid X receptor (RXR) modulators, using a recently developed method, the bidirectional molecule generation with alternate learning (BIMODAL). The program code is freely available here.

2.Materials

2.1.Computational Methods

• All calculations were performed using Python 3.7.4 in Jupyter Notebooks. The models rely on PyTorch and RDKit.
• After installing Anaconda and Git, we can run the code below:

git clone https://github.com/ETHmodlab/de_novo_design_RNN.git
cd <path\to\folder>
conda env crate -f environment.yml
conda activate de_novo
cd example
jupyter notebook

2.2.Data

• To emulate a realistic scenario, we provide a tool molecule library containing four RXR modulators (Fig. 2). Molecule 1 is bexarotene, a pharmacological RXR agonist. Molecules 2–4 were obtained from ChEMBL and have a potency on RXR (expressed as EC50, IC50, Ki, or Kd) below0.8μM. This set of bioactive compounds (available in the repository, under “/exam- ple/fine_tuning.csv”) will be used to generate a focused library of de novo designs.