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Drug development is a notoriously expensive and time-consuming process. The advent of Artificial Intelligence (AI) has introduced transformative possibilities, significantly expediting various stages of drug development. 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, have provided a major scientific breakthrough in the area of computational drug development.

The Drug Development Process

●      Identifying and Validating Targets

The journey of drug development begins with the identification and validation of a biological target. A target is typically a gene or protein to which a drug binds to exert its therapeutic effect. The majority of these targets are proteins. Not all proteins are suitable targets; only those with specific sites where drugs can effectively dock and act are termed "druggable" proteins.

●      Discovery Phase

In the discovery phase, a target protein sequence is analyzed to find the most suitable drug candidate from a vast library of small molecules. This library contains the structural data of numerous potential drugs. The analysis assumes that both the target protein's and the drug's structures are known. If not, computational models are employed to predict the binding sites on the protein.

●      Pre-Clinical and Clinical Phases

Once a potential drug and its target are identified, the research progresses to the pre-clinical phase. Here, the drug candidates are tested in vitro (using cells) and in vivo (using animals) to assess their safety and toxicity. Following successful pre-clinical tests, the drug enters the clinical phase, which involves human trials to evaluate efficacy and safety across increasing numbers of patients. Finally, the drug must undergo regulatory approval and post-market surveillance to ensure ongoing safety and efficacy.

Challenges in Traditional Drug Development

Traditional drug discovery is fraught with high failure rates and substantial costs. The discovery phase alone can significantly limit the number of potential drugs progressing to pre-clinical and clinical testing due to these constraints.

The Impact of AI on Drug Development

• Accelerating Target Discovery : AI has the potential to revolutionize target discovery and drug-target interaction studies by dramatically reducing the time required and increasing the accuracy of predictions. AI can also save substantial costs by avoiding the need for many preliminary laboratory experiments. 

• AlphaFold and RoseTTAFold: Breakthrough AI Tools : Two AI-based tools, AlphaFold and RoseTTAFold, have marked significant advancements in computational drug development.  These tools leverage deep neural networks to predict the three-dimensional structures of proteins with high accuracy.
• AlphaFold 3 and RoseTTAFold All-Atom : The newer versions of these tools, AlphaFold 3 and RoseTTAFold All-Atom, offer enhanced capabilities. Unlike their predecessors, these versions can predict not only static protein structures and protein-protein interactions but also the structures and interactions involving any combination of proteins, DNA, RNA, small molecules, and ions. They utilize generative diffusion-based architectures to predict structural complexes, significantly improving the prediction accuracy for drug-target interactions.

Predictive Performance and Limitations

In tests involving 400 interactions between targets and small molecule drugs, AlphaFold 3 demonstrated a 76% accuracy rate in predicting interactions, compared to 40% by RoseTTAFold All-Atom.

• Accuracy Constraints: These tools can achieve up to 80% accuracy in predicting interactions, but their accuracy significantly decreases for protein-RNA interaction predictions.
• Limited Scope: AI tools primarily assist in the target discovery and drug-target interaction phases. The entire drug development process still requires pre-clinical and clinical phases, and there is no guarantee that AI-derived molecules will succeed in these stages.

• Model Hallucinations: Diffusion-based architectures can suffer from "model hallucinations," where insufficient training data leads to incorrect or non-existent predictions.
• Restricted Access: Unlike previous versions of AlphaFold, DeepMind has not released the code for AlphaFold 3. This limits independent verification, broad utilization, and studies of protein-small molecule interactions.

India's Position in Computational Drug Development 

• Infrastructure and Expertise: Developing cutting-edge AI tools for drug development requires substantial computational infrastructure, particularly fast Graphics Processing Units (GPUs) capable of handling extensive tasks and longer sequences. These GPU chips are costly and have a rapid obsolescence due to the constant advancements in hardware technology. India currently faces a shortage of such large-scale computing infrastructure.
• Skilled Workforce: Another significant challenge is the lack of skilled AI scientists in India, in contrast to countries like the U.S. and China. Despite India's rich history in protein X-ray crystallography, modeling, and other fields of structural biology, this skill gap has hindered the establishment of a first-mover advantage in AI-driven drug development.
• Emerging Opportunities: However, India is not without potential. The growing number of pharmaceutical companies in the country presents an opportunity for India to become a leader in applying AI tools for target discovery, identification, and drug testing. By investing in computational infrastructure and nurturing AI talent, India can leverage its scientific expertise to make significant contributions to the global drug development landscape.

Conclusion

India, with its rich history in structural biology, has the potential to become a significant player in computational drug development. To achieve this, the country needs to invest in advanced computational infrastructure and cultivate a skilled workforce in AI. By doing so, India can harness the power of AI to expedite drug development, ultimately benefiting global healthcare.

By addressing these challenges and capitalizing on emerging opportunities, the future of drug development promises to be faster, more accurate, and cost-effective, driven by the innovative applications of AI technology.

Source- The Hindu