The Impact of AI and Data Analytics in Pharma Research
Data Analytics Pharma

The Impact of AI and Data Analytics in Pharma Research

By Rajarshi January 03, 2024 - 82 views

AI in pharma research has the potential to be a veritable game-changer for the entire sector. Data analytics in pharmaceuticals along with other innovations like data-driven research and AI/machine learning in pharma have made it comparatively easier to develop new drugs and tackle emerging diseases. Biopharma research remains expensive and lengthy although AI can play a vital role in enabling higher probabilities of success and boosting productivity. 

How AI and Data Analytics are Indispensable for Pharma Research

Here are a few ways in which AI in pharma research can be indispensable for the industry soon.

  • AI helps in accelerating drug research and identifying promising compounds/elements/targets at each step throughout the value chain. This enables more successful and fewer experiments to be conducted in laboratories to achieve good leads. 
  • AI-based drug discovery has grown immensely over the last few years. For instance, AstraZeneca’s partnership with BenevolentAI led to identifying several new targets in the idiopathic pulmonary fibrosis domain with the scope broadening to other TAs or therapeutic areas as well. Exscientia collaborated with Sumitomo Dainippon Pharma to identify DSP-1181 for compulsive disorders in less than 25% of the time usually required for drug discovery procedures. 
  • This suggests a strong case for leveraging machine learning in pharma along with data analytics in pharmaceuticals. AI-enablement abilities are also enabling benefits like curated labelled cell images, the ready supply of talent for building pipelines in research, training, assurances of data security, screening of data, and strategies for maximising patient impact. 
  • Data analytics in pharmaceuticals and AI tools enable gathering insights from various data sources that are usually used for the generation and identification of novel target hypotheses. Automated image analysis for cellular assays is also used for hit identification. Property and structure prediction at a molecular level is executed for novel target proteins. DMPK prediction is executed with public and internal data at the preclinical safety stage issue. 
  • Hypothesis generation tasks usually take several weeks in research. This is now transformed into curated lists in a few minutes by fusing real-world data, genomics information, and scientific literature via the target identification knowledge graph. 
  • There is a higher acceleration for generating protein structures to identify targets and take care of large-molecule-structure inferences. 
  • Computer vision technology is also enabling higher acceleration in screening and plate-image analysis with more accuracy. Deep learning approaches are also being leveraged for target validation and hit identification. 
  • In silico medicinal chemistry, speedier high-throughput molecular screening is already being enabled with molecular property prediction through iterative screening loops and better-hit identification. 
  • Other areas where there are benefits include faster compound design times and drug delivery optimisation along with knowledge-graph-based generation of hypothesis and drug repurposing, in addition to prioritising indications for pursuing the novel MoAs or mechanisms of action. 
  • Machine learning in pharma research also works to build pipelines to learn how cells respond to every type of chemical structure. Algorithms scan library compounds and predict the plates that should be prioritised for identifying the highest number of hits on the next screen. These recommendations are automatically used and queued in the HTS next round. The algorithm keeps learning from outputs in the real world. Recommendations also drive scientists to explore newer chemical spaces and start the downstream screening procedures faster. 

AI in pharma research will enable the creation of feedback loops for further refining the predictive abilities and stability of AI algorithms. They will also inform experimental design functions accordingly. Through analytics and data science tools, pharma can capture the entire value of the present portfolio and create mechanisms and IP for driving research in the future. AI-drug discovery is already taking place with several companies building their pipelines.

Biopharma entities are also developing top-down and executive strategies where AI-backed discovery can be a vital indicator and enabler of performance in the future. Automated image analysis or lead optimisation will be bolstered along with the collection of experimental data in a reusable manner, automated screening algorithms linking molecular descriptions with hits or desired outputs, blueprinting, enabling better testing and learning solutions for product delivery and designing new screening protocols.

AI is already transforming the research space through the application of machine learning and data science to huge data sets, enabling swifter discoveries of newer molecules. It enables cross-referencing of published scientific literature with alternate sources of data (clinical trial data, conference abstracts, public databases, and unpublished data) to surface therapies that are promising. Medicines can be delivered in months at times instead of several years as a result.

AI can also help lower clinical trial costs and cycle times while enhancing overall clinical development outcomes considerably. ML and AI are already being used for automatically generating study protocols while NLP (natural language processing) is being used to scale up manual tasks.

AI algorithms can also ensure continual clinical data cleaning, coding, aggregation, management, and storage. Through automation and centralisation of intakes for adverse event reports backed by AI-backed technologies like NLP and OCR (optical character recognition), case documentation workloads are considerably reduced for expediting investigative processes.

These are only a few of the widespread benefits that data analytics, AI, and ML can bring to the table for life sciences and pharmaceutical companies, especially in terms of research and development.


What role will AI play in optimising clinical trials and research methodologies, and how is this expected to impact the pharmaceutical industry in 2024?

AI will play a huge role in the optimisation of research methodologies and clinical trials in the future. This will have a positive impact on the pharmaceutical industry in 2024 and beyond since AI will optimise patient recruitment, predict the efficacy of treatments, automate data analysis, and boost safety tracking. It will also accelerate trial procedures while lowering costs and enhancing data quality. This will lead to more personalised and successful clinical trials.

How will integrating AI and data analytics accelerate drug discovery processes within the pharmaceutical industry in the upcoming year?

Drug discovery processes within the pharmaceutical industry can be accelerated in the upcoming year through the integration of data analytics and AI. This will be possible through the prediction of drug-target interactions, evaluation of the safety and efficacy of drugs that are repurposed, and identification of newer options for treatments. Potential biomarkers can be identified while researchers can easily analyse big data sets and design new molecules while forecasting the efficacy levels of potential drug candidates accordingly.

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