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AI in drug development UPSC NOTE

 What are target proteins and how are they identified?

  • 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

AlphaFold 3 and RoseTTAFold All-Atom

  • 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. 

  • Published recently, the new avatars of AlphaFold and RoseTTAFold, called AlphaFold 3 (developed jointly by Isomorphic Labs, a DeepMind spinoff) and RoseTTAFold All-Atom, respectively, take the capability of these tools to an entirely new level. 

  • 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

Drawbacks

  • 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.

India’s stand

  • 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.

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Learnerz IAS | Concept oriented UPSC Classes in Malayalam: AI in drug development UPSC NOTE
AI in drug development UPSC NOTE
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