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AI in Drug Development

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May 23, 2024

Why in news?

The advent of Artificial Intelligence (AI) has opened up a world of possibilities with respect to fast-tracking drug development.

What is the process of developing the drugs?

  • The process of developing a drug starts with identifying and validating a target.
  • Target - A target is a biological molecule (usually a gene or a protein) to which a drug directly binds in order to work.
  • Druggable proteins - Proteins are mainly used as targets, proteins with ideal sites where drugs can go and dock to do their business are druggable proteins.
  • Discovery phases - Target proteins are identified in the discovery phase, wherein a target protein sequence is fed into a computer which looks for the best-fitting drug out of millions in the library of small molecules.
  • Pre- clinical phase - In pre-clinical phase potential drug candidates are tested outside a biological system, using cells and animals for the drug’s safety and toxicity.
  • Human trial- After this, as part of the clinical phase, the drug is tested on a small number of human patients before being used on more patients for efficacy and safety.
  • Approval phase - Finally, the drug undergoes regulatory approval and marketing and post-market survey phases.
  • Computational phase- These methods avoid the need for preliminary laboratory experiments, which are often time-consuming, costly, and have high failure rates. Once a suitable drug-target interaction is identified, the process moves to the pre-clinical phase.

How does AI can help this process?

  • Accelerated target discovery- AI models can process vast amounts of data quickly, identifying potential targets and predicting their interactions with drugs much faster than traditional methods.
  • Enhanced accuracy- AI tools, such as AlphaFold and RoseTTAFold, developed by DeepMind and the University of Washington respectively, have made significant strides in predicting the three-dimensional structures of proteins.
  • Forecast dynamic interactions- The latest versions, AlphaFold 3 and RoseTTAFold All-Atom, go beyond predicting static structures to forecasting dynamic interactions, including those involving small molecules, DNA, RNA, and ions.
  • Increase in accuracy- In comparative tests, AlphaFold 3 demonstrated a 76% accuracy in predicting interactions between targets and small molecule drugs, significantly higher than the previous versions.
  • Generative diffusion based architecture- These AI models improve the prediction of structural complexes, enhancing the understanding of how drugs interact with their targets.

AI in drug development

  • Alpha Fold- It predicts the 3D structure of proteins based on their amino acid sequence, it can predict protein shape with atomic accuracy almost instantly.
  • RoseTTAFold- It is a “three track” neural network, meaning it simultaneously consider patterns in protein sequences, how a protein’s amino acids interact with one another and protein’s possible three-dimensional structure
  • AlphaFold 3- It goes beyond proteins to a broad spectrum of biomolecules including DNA, RNA, and even small molecules, also known as ligands, which encompass many drugs.
  • RoseTTAFold All-Atom- It is a neural network that can biomolecular assemblies that contain proteins, nucleic acids, small molecules, metals and covalent modifications.
  • It is faster than other models which can make accurate predictions for protein-small molecule complexes and covalent changes to proteins.

What are the advantages?

  • Target identification - Computer searches a library of small molecules for the best-fitting drug to a target protein sequence.
  • Assay development - It can help identify targets and develop assays to test compounds.
  • Preclinical testing - It can help determine the effectiveness of compounds in preclinical testing.
  • Drug delivery - It can help develop more efficient drug delivery systems and select formulation approaches to improve drug solubility and absorption.
  • Molecular structure prediction - Generative AI can train models to generate new molecular structures, which scientists can use to predict potential drug candidates.
  • Saving the time & Money -It can increase the accuracy of prediction of interaction between a drug and its target, and saving money.

What are the limitations?

  • Accuracy limitations- AI tools can provide up to 80% accuracy in predicting interactions and the accuracy comes down drastically for protein-RNA interaction predictions.
  • Limited scope - The tools can only aid a single phase of drug development, target discovery and drug-target interaction.
  • Model hallucinations-AI models, particularly those based on diffusion architectures, can sometimes produce incorrect or non-existent predictions due to insufficient training data.
  • Restricted accessibility- Unlike AlphaFold, DeepMind has not released the code for AlphaFold 3, restricting its independent verification, broad utilisation and use.
  • Skilled workforce- There is a shortage of skilled AI scientists in India compared to countries like the U.S. and China, this gap hinders the ability to capitalize on AI advancements in drug development.
  • Computing infrastructure- Developing sophisticated AI tools requires robust computing infrastructure, particularly high-speed GPUs. These are expensive and quickly become outdated as new models are released.

What lies ahead?

  • India has a growing pharmaceutical industry and a rich history in structural biology fields like protein X-ray crystallography and modeling.
  • With investment in infrastructure and training, India has the potential to become a leader in applying AI tools for drug discovery and testing.

 

References

  1. The Hindu | The use of AI in drug development
  2. Economist | Artificial intelligence is taking over drug development
  3. The Week | How AI is changing drug development 
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