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.