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Mahesh

17/05/24 06:00 AM IST

The use of AI in drug development

In News
  • Drug development is an expensive and time-consuming process. However, the advent of Artificial Intelligence (AI) has opened up a world of possibilities with respect to fast-tracking drug development.
About Process
  • The process of developing a drug starts with identifying and validating a target.
  • A target is a biological molecule (usually a gene or a protein) to which a drug directly binds in order to work.
  • The overwhelming majority of targets are proteins. Only those proteins with ideal sites where drugs can go and dock to do their business are druggable proteins.
  • 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 for which the structures are stored in the computer.
  • The process assumes that the structures of the target protein and drug are known.
  • If not, the computer uses models to understand the sites where a drug can bind.
  • This discovery process avoids time-consuming laboratory experiments that require expensive chemicals and reagents and have a high failure rate.
  • Once the suitable protein target and its drug are identified, the research moves to the pre-clinical phase, where the potential drug candidates are tested outside a biological system, using cells and animals for the drug’s safety and toxicity.
  • 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.
  • Finally, the drug undergoes regulatory approval and marketing and post-market survey phases.
  • Due to a high failure rate, the discovery phase limits the number of drugs that pass and carry on to the pre-clinical and clinical phases.
AI Significance
  • AI has the potential to revolutionise target discovery and understand drug-target interaction by drastically cutting down time, increasing the accuracy of prediction of interaction between a drug and its target, and saving money.
  • The development of two AI-based prediction tools, AlphaFold and RoseTTAFold, developed by researchers at DeepMind, a Google company, and the University of Washington, U.S., respectively, has provided a major scientific breakthrough in the last four years in the area of computational drug development.
  • Both tools are based on deep neural networks.
  • The tools’ neural networks use massive amounts of input data to produce the desired output — the three-dimensional structures of proteins.
  • The significant difference between the upgraded versions and their previous forms is their capability to predict not just static structures of proteins and protein-protein interactions but also their ability to predict structures and interactions for any combination of protein, DNA, and RNA, including modifications, small molecules and ions.
  • Additionally, the new versions use generative diffusion-based architectures (one kind of AI model) to predict structural complexes.
Challenges
  • With all the promise and potential in drug development, AI tools have limitations. For example, the tools can, at best, provide up to 80% accuracy in predicting interactions (the accuracy comes down drastically for protein-RNA interaction predictions).
  • Second, the tools can only aid a single phase of drug development, target discovery and drug-target interaction.
  • It will still have to go through the pre-clinical and clinical development phases, and there is no guarantee that the AI-derived molecules will result in success in those phases.
  • Third, one of the challenges with diffusion-based architecture is model hallucinations, where insufficient training data causes the tool to produce incorrect or non-existent predictions.
  • Finally, unlike the previous versions of AlphaFold, DeepMind has not released the code for AlphaFold 3, restricting its independent verification, broad utilisation and use for protein-small molecule interaction studies.
Case of India
  • Developing new AI tools for drug development requires large-scale computing infrastructure, especially ones with fast Graphics Processing Units (GPUs) to run multiple tasks with longer sequences.
  • GPU chips are expensive, and with newer and faster ones being produced by hardware makers every year, they have a quick expiration date.
  • India needs such large-scale computing infrastructure.
  • That, along with a lack of skilled AI scientists, unlike in the U.S. and China, is the second reason why researchers in India could not establish a first-mover advantage in developing AI tools for drug development despite the country having a rich history in protein X-ray crystallography, modelling and other fields of structural biology.
  • However, with a growing number of pharmaceutical organisations, India can lead the way in applying AI tools in target discovery, identification, and drug testing.
Source- The Hindu

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