AI - De Novo Molecule Design

AI - De Novo Molecule Design

Develop a generative LSTM deep learning model for COVID-19 ligand molecule design and screening using Python, Google Colab and AutoDock Vina.

Develop a generative LSTM deep learning model for COVID-19 ligand molecule design and screening using Python, Google Colab and AutoDock Vina

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Image Credit: Sagittaria — stock.adobe.com

Needle in a Haystack

On average, it takes ten years and costs $2.6 billion dollars to take a drug from the point of understanding the root cause of a disease to its availability in the marketplace. A large portion of this time and effort/cost is because we are literally looking for a needle in a haystack. We are looking for the one molecule that can turn off a disease at the molecular level in a solution space of between 10³⁰ to a google (yes, 10¹⁰⁰) synthetically feasible molecules. The chemical solution space is too vast to be efficiently screened for the particular molecule of interest. Pharmaceutical compound repositories contain only a fraction of the synthetically feasible molecules for research in a wet lab.

Computational de novo drug design can be used as a tool to explore this vast chemical space and synthesize new, never before designed, molecules. Computational drug design can greatly reduce the time spent in the discovery phase; thereby enabling both faster time to market and lower medicine costs.

The Target

The majority of COVID-19 media images have been of the coronavirus with its protruding spikes. These spikes are the S-proteins that bind to human cells and allow the virus to infect them.

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Photo by CDC on Unsplash

When the virus fuses with a cell, viral genetic material is released into the cell in the form of viral RNA. The viral RNA highjacks our own cells to create the viral proteins needed to make additional copies of itself. Those copies are then released from the cell and used to infect other cells. There is an important protein involved in this process, the main protease. The main protease is responsible for creating the functional proteins needed to assemble other copies of the virus.

If we can find a drug that binds with the main protease and stops it from creating new viral proteins, viral replication can be slowed or stopped. In this article, we explore use of deep learning AI for generating new molecules (aka ligands) that bind to the COVID-19 main protease.

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COVID-19 Main Protease — Cartoon Form (left), Surface Form (right)

Solution Overview

The approach is to create an AI deep learning neural network that will learn how to create ligand molecules. Then a generative model is built from the trained neural network to design new synthetic molecules. AutoDock Vina is used to perform virtual screening of the new molecules to assess their effectiveness for binding to the COVID-19 main protease.

The generated molecules with the best binding scores are then used as the transfer learning dataset to further refine the neural network model toward creating more effective inhibitors of the COVID-19 main protease. Using the updated model, a second generation of molecules is created and screened for their binding assessment.

The high-level workflow:

  • Obtain and prepare the molecule training data
  • Create a neural network model and train it to make molecules
  • Develop a generative AI model and create new molecules
  • Virtually screen the new molecules against the COVID-19 main protease
  • Use transfer learning to train the neural network on a more specific task
  • Generate new AI molecules using the generative model
  • Virtually screen the new molecules against the COVID-19 main protease

ai covid19 machine-learning tensorflow biotechnology

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