The use of AI in drug development
- May 17, 2024
- Posted by: OptimizeIAS Team
- Category: DPN Topics
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The use of AI in drug development
Sub: Science and tech
Sec: Awareness in IT and computers
Context:
- The advent of Artificial Intelligence (AI) has opened up a world of possibilities with respect to fast-tracking drug development.
How does the process start?
- 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.
- 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.
- Computers use 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 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.
How can AI help this process?
- AI has the potential to revolutionize 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 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 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.
- Upgraded Versions:
- 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.
What are the drawbacks?
- The tools can provide up to 80% accuracy in predicting interactions (the accuracy comes down drastically for protein-RNA interaction predictions).
- The tools can only aid a single phase of drug development, target discovery and drug-target interaction.
- Insufficient training data causes the tool to produce incorrect or non-existent predictions.
- Unlike the previous versions of AlphaFold, DeepMind has not released the code for AlphaFold 3, restricting its independent verification, broad utilization and use for protein-small molecule interaction studies.
Aspects for 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.
What are Deep neural networks (DNN)?
- Deep neural networks (DNN) is a class of machine learning algorithms similar to the artificial neural network and aims to mimic the information processing of the brain.
- Deep neural networks can recognize voice commands, identify voices, recognize sounds and graphics and do much more than a neural network.
What is a Graphics Processing Unit (GPU)?
- The graphics processing unit (GPU) in your device helps handle graphics-related work like graphics, effects, and videos.
- Integrated GPUs are built into your PC’s motherboard, allowing laptops to be thin, lightweight, and power-efficient.
About AlphaFold and RoseTTAFold:
- AlphaFold is an artificial intelligence program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. The program is designed as a deep learning system.
- RoseTTAFold is a “three-track” neural network, meaning it simultaneously considers patterns in protein sequences, how a protein’s amino acids interact with one another, and a protein’s possible three-dimensional structure.