By: Satyakam Mohanty, Head of Leni and Mosaic Applied Intelligence, LTI

Several industries have tried, tested and transformed themselves with AI. This has allured big pharma companies to invest in its potential. From improving candidate selection for clinical trials to accelerating new drug development, AI is quickly becoming the game-changer in this dynamic industry. Top pharma companies now have Chief Digital Officers and Chief Data Officers in their leadership team, which shows pharma is becoming AI-centric.

Data-driven drug discovery

The integration of AI reduces the duration of drug generation. It also assists in predicting the efficacy and safety of a molecular form by providing mechanistic insight into the disease, and its possible targets. It gives researchers a broader chemical pallet to select the best molecules for drug testing and delivery. With millions of clinical data points already available, AI algorithms also help in drug re-purposing for new therapeutic purposes or alternative diseases.

Enhancing drug development

AI facilitates researchers to study, process and find raw data to enhance drug development for patients. AI has also made inroads into marketing products and using data science for companies, which allows a customer to make an informed decision. Data analytics results in improving the speed of drug discovery processes and reduces research costs. Experts believe that AI will assist in better decision making, improve delivery and efficiency of the pharma industry more than ever before.

Circulating potent data for R&D pipelines

AI helps pharma companies go above and beyond data interpretation and information on competition as well. This allows them to identify and analyze their core strengths. It also aids in unearthing new needs, which guides the R&D process, and provides a platform to set priorities and benchmarks. Predictive analytics is actually being used to help find candidates for clinical trials. It combines elementary data, genetic information, e-records and even social media data to analyze a candidate accurately. Furthermore, the completion of complex and long drawn R&D projects can now be accurately predicted. It results in speeding up of clinical trials and managing several R&D portfolios efficiently

Personalizing medicine

Time and R&D costs play a huge role in pharma and biopharma industry. AI improves efficiency, processes, analysis and drugs-to-market time, It offers tremendous potential across the drug development value chain. AI-powered drug discovery efforts are enabling big pharma and biotechnology companies to streamline R&D efforts. This includes calculating vast patient datasets into digestible and tangible information, identifying personalized/ precision medicine opportunities or forecasting potential responses to new drugs.

Injecting growth and planning into sales

With AI, customer experience is better than ever before with multichannel engagement. They can predict the best channels, messages and timing for every single customer. AI helps in customer segmentation and boosts the productivity of sales reps through planogram solutions. Above all, it helps companies to assess their performance through benchmarks.

AI – the new DNA of the pharma ecosystem

AI is now deeply ingrained in the pharma ecosystem and has enhanced drug invention, development and cost savings in the long-run. Despite clear benefits, AI and machine learning still needs to be implemented at all levels and business functions to reap its enormous benefits. As more pharma companies get to grips with AI-enabled processes, it’s likely to evolve far beyond the existing applications – which would be nothing short of transformational.

Smart solutions

Stay tuned, in this riveting blog series, I will uncover the various facets of the role played by AI & analytics in life sciences & pharma industry.

Other topics covered in the series:

The AI Remedy Series: Prescriptions for Intelligent Transformations
The AI Remedy Series: NLG – Accelerating Pharma’s Data-to-Decision Journey
The AI Remedy Series: Decoding the DNA of Data Deluge with NLP