AI Transforms Pharma: Reducing Human Error, Accelerating Drug Discovery

Artificial intelligence is revolutionizing the pharmaceutical industry by reducing human error and speeding up drug discovery processes. By automating data analysis, improving accuracy, and predicting outcomes, AI enhances both research and manufacturing. This transformative technology allows for more efficient drug development, ensuring quicker access to effective treatments. AI's integration into pharma is paving the way for groundbreaking innovations in medicine.

The incorporation of AI in pharmaceutical manufacturing and research helps minimize human error and accelerate drug discovery, stated Dr. Alok Aggarwal, CEO and chief data scientist at Scry AI, a company specializing in AI-driven enterprise applications.

The discovery of small molecules is especially appealing because abundant high-quality data available in public and industry databases can train AI algorithms for more accurate predictions. Furthermore, the properties of small molecules are well-documented, making it easier to describe their chemical structures and understand their interactions and potential toxicity, he explained.

Dr. Aggarwal also mentioned that the traditional methods of discovering, designing, and producing new drug molecules are highly complex, which has led to leveraging AI for these tasks. AI is applied in areas such as protein structure prediction, drug design, drug–protein interaction analysis, and drug repurposing.

In the field of polypharmacology, AI is used to study a drug’s tendency to interact with living tissues and produce off-target side effects. Since predicting the final folds of proteins is critical for determining their behavior, deep learning networks like AlphaFold2 and RoseTTAFold have made significant progress in this area. The next step is identifying low-toxicity compounds that can bind to disease-causing proteins and neutralize them. AlphaFold2 has already shown promise in this regard, though the research is still in its early stages.

AI expert systems, combined with organic chemistry and retrosynthesis, are speeding up drug discovery and manufacturing. Drug repurposing allows pharmaceutical companies to bypass phase I clinical trials, saving costs. Deep learning networks and other AI algorithms are increasingly being used to study the relationship between drugs and diseases. In one study, researchers used deep learning to repurpose drugs effective against SARS-CoV, HIV, and influenza, identifying 13 drugs for further investigation.

In 2016, the AI model DeepTox outperformed all other methods in predicting a compound’s toxicity by identifying 2,500 toxicophoric features in chemical descriptors with high accuracy.

AI is also being employed in clinical trial design to reconcile large, disparate datasets. Using domain-specific AI systems, researchers are creating a comprehensive view of each patient, which then trains AI algorithms to improve the matching of patients to clinical trials, ultimately enhancing the selection of trial participants, Dr. Aggarwal noted.

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Source: pharmabiz‍