How Biopharma Companies Improve Drug Research and Development Using AI

The COVID-19 pandemic has heightened interest in applying artificial intelligence (AI) throughout the health sciences industry, from research and development to manufacturing, supply chain, and commercial operations.

FREMONT, CA: In the future of health, AI will speed up the development of new medicines and vaccinations. AI can assist find and validate genetic targets for drug research, create novel molecules and accelerate drug development.

The most effective use of AI is going to be a strategic deployment across the entire organization. With cross-functional collaboration, a governance framework that prioritizes business goals, and a uniform approach to cyber and compliance challenges, biopharma businesses will be able to maximize their AI investments.

Research and development

Drug discovery is a complex, manual, and capital-intensive process that can be automated or enhanced by AI. Biopharma companies are already testing AI to speed up drug discovery. Firms might utilize AI models to identify and assess objectives in the next five years to strengthen predictive capabilities. This might drastically speed up the drug discovery process.

There is a rising need for solutions that may transform and accelerate target identification and validation. AI imaging can identify changes in cell morphology that a microscope cannot perceive. The use of AI to analyze knowledge graphs could help grasp intricate interactions between proteins and chemicals.Using AI to interpret phenotypic changes in cells via imaging could help validate targets through functional genomics investigations (noncoding changes that affect expression).  

To avoid costly late-stage drug development failures, acquiring genetic evidence for target identification will become a strategic goal.

Some startups are already pioneering generative modelling for small molecule creation and protein engineering collaboration with established biopharma businesses through cooperation with established biopharma businesses. Simultaneously, biopharma firms are developing their tools to assist medicinal chemists in their work. In the next five years, generative modeling may become a vital tool in computational chemistry, allowing companies to investigate new areas and expand their drug candidate pool.

Creating trial artefacts today entails manually entering data into many systems. This causes inconsistencies, errors, and rework, slowing trial execution. AI-powered digital data flow solutions could normalize trial data from diverse source systems and documents for downstream systems. These data elements can then be used to auto-generate reports and analysis and trial artefact material. Some businesses are already testing AI to handle clinical trial data better.

Over the coming decade, clinical trial designers will increasingly target patient populations to establish efficacy, minimizing development time and failures. Data normalization and integration (genotypic, imaging, clinical records, and epidemiological) will be increasingly used by AI to improve patient stratification and trial success. After the treatment is approved/commercialized, AI-enabled companion diagnostics techniques (e.g., those used to screen for biomarkers) could better understand the response profile.

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