TeroGen

Implementation of the terpenoids generation used in "Bio-inspired Chemical Space Exploration of Terpenoids"

Introduction

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Source code used to create, train and sample a bio-inspired terpenoids generation model described in Bio-inspired Chemical Space Exploration of Terpenoids.

The model is divided into two relatively independent parts, a Reactor, which conducts metadynamics simulations to explore the reaction space of given carbocations and a Decorator, which predicts the decorating sites and groups for a given skeleton.

Set up

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For Reactor xtb is required. In some scripts of Decorator Spark 2.4 is required (and thus Java 8). Reactor was tested on Linux with CPUs and Decorator was tested with a Tesla V-100. You will wish to install TeroGen in a virtual environment to prevent conflicting dependencies.

conda create -n terogen python==3.6.13
conda activate terogen
sh install.sh

General Usage

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Carbocation Reactor (./reactor)

Any arbitrary molecule can be used as the initial structure for metadynamics sumilations, while herein for tergenoids generation, the isoprenoid carbocations were used.

reactor.sh: This script will conduct medadynamics sumilations with specific initial structure and parameters. The output is a reactant-product list with energetics properties in tsv format. deprotonation.py: This will quench carbocations by exhaustive deprotonation and also output a reactant-product list in tsv format.

It will take about 10 hours on 12 CPUs (Xeon E5-2609 1.70GHz) to run the demo provided in the script.

Skeleton Decoration (./decorator)

There are two step for skeleton decoration, sites prediction and groups prediction. First, the decorating sites were predicted with Transformer model trained using OpenNMT and PyTorch and then, the R-groups were predicted with the RNN-based model proposed by Arús-Pous et al..

Sites prediction:

One need to first download the data and unzip under terogen/Decorator/site_prediction, in which the checkppints can be used to do the prediction directly and the dataset can be used to train and test the model by user.

skeleton_extraction.py: This is used to extract the carbon skeleton from the terpenoids structure. site_prediction.sh: This script is used to train and test the sites prediction model.

Groups prediction:

This model was analogous to the scaffold decorator proposed in "SMILES-based deep generative scaffold decorator for de-novo drug design"

One need to first download the data and unzip under terogen/Decorator/group_decoration, in which the checkppints can be used to do the prediction directly and the dataset can be used to train and test the model by user.

group_prediction.sh: This script is used to train and test the sites prediction model.