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Pharmaceutical Applications

Pharmaceuticals and Artificial Intelligence

 In various fields like Telecommunication, Healthcare, Insurance, Manufacturing, and others,

we process Petabytes of scale data to give an idea. Likewise, the Pharmaceutical industry

is also being aided by it for effective functioning. Petabytes of scale data help to solve

multiple business processes and improve efficiency across the board.

Research and development

      Discovery from drug to real life use

      Identify sources of clinical data


      Integrate data into big data


      Link datasets for research


      Gain input about various drugs

Clinical Trials

      Collect info about genetic details, personality

       traits, disease status

      Analyze if patient is fit for clinical trial


      Perform shorter and cheap trials

Drug Discovery

      Use predictive modelling for drug discovery

      Easily predict drug allergies, toxicity or



Drug Reactions

     Use sentiment analysis to read drug reactions

     Use natural language processing


     Gather information about any reactions


      Simplify drug reactions



Precision Medicine

      Enable precise medicine

      Diagnosis and treatment of orders


      Develop personalized medicinessuitablefor an


      Predict susceptibility to certain disorders and

      enhance disorder detection

Sales and Marketing

      Analyze the geographical locations with       

       maximum ​promoted medicines

      Make key decisions in marketing and sales


      Analyze about customer behavior


      Analyze ad campaigns and customer retention

      Perform predictive analysis for industry trends

How Sparkflows aids in Pharma


The process of cleaning data consumes an enormous time manually but Sparkflows makes data preparation and cleaning fast and easy.


Sparkflows takes help from common open source libraries and toolkits to provide strong and dependable understanding of machine and deep learning resources.


After the project is ready, Sparkflows helps users to test the accuracy of prediction by using cross validation that divides data into two subsets- Training and Testing.


Sparkflows provides models with real data that reflects the actual population where the drug would be used.

Data Analytics and AI for improved Drug Developement

Diaognosis & Identification of diseases

Sparkflows helps solve the biggest challenge of diagnosis and identification of diseases by machine learning development.

Personalized Medicine

Sparkflows uses machine learning and predictive analytics in customizing treatment to a person's unique medical history.

Drug discovery and manufacture

Sparkflows uses machine learning for early drug discovery like new drug compounds, discovery technology, next generation sequencing and more.

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Clinical Trials

Sparkflows makes this process very easy by using predictive analysis on a wide range of data to target populations more quickly.

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Electronic Health Records

Sparkflows supports vector machines and optical character recognition as essential components of machine learning systems for document classification.

Specific Use Cases of Sparkflows

Accurately identifying patients for clinical trials

Data science, machine learning, and AI can help to quickly and precisely identify patients who would be fit for a particular trial via advanced analysis of medical records through natural language processing (NLP) or by exploring geographically. These techniques can examine the interactions of potential trial members’ specific biomarkers and current medication to predict the drug’s interactions and side effects, avoiding any potential complications.

The Future of Computational Biochemistry


Computational biochemistry allows drug-makers to cut down a great portion of the test tube experiments. Instead, a computer is used for the protein and tests all of its atomic interactions. Such an analysis will allow researchers to take to the next stage of testing with a smaller list of leads. Deep learning models drastically save the expenses of Stage 1 trials.

Supply chain and Manufacturing.


Pharmaceutical companies can better forecast demand and distribute products more efficiently through the use of Data science, machine learning, and AI techniques. For manufacturing, pharmaceuticals can use machine learning to control the cost of equipment maintenance and make the way for self-maintenance through artificial intelligence. Predictive maintenance is widely used in any business with high-capital assets.

Identifying compounds


An easy option for pharmaceutical companies can be to leverage machine learning techniques to cut down through literature and journal publications using NLP and also to pre-screen for the most effective potential compounds to prioritize their time.

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