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AI for Drug Discovery

Streamlining drug discovery through AI by identifying drug targets, accelerating the testing and validation process, predicting drug efficacy and toxicity

Accuracy of upto 95% in new drug molecule discovery and feasibility


25%-30% reduction in costs for preclinical and clinical development


Reduction in time spent for new molecule screening

FASTER TIME TO REVENUEFaster time to market and revenue realization

Business Challenge

It is estimated that close to $2.6 Billion is spent in new drug discovery starting from discovery of new drug molecules, to development, to clinical trials and FDA approval. Most of this cost is owing to the complexity of molecule screening process, high development costs due to increasing input costs, levels of complex clinical trials to be conducted. 
While these are direct costs, there are hidden indirect cost to company such as lengthy time to market anywhere close to ten years for a new drug, lower time to revenue, all of which leads to prolonged sales cycle and therefore are indirect costs. Data science and AI has made significant strides in simplifying and automating the process of drug discovery and clinical trials.

Our AI drug discovery use cases are in the following areas

  • AI based virtual screening of molecules
  • AI enabled de-novo drug design
  • AI based drug combination optimization
  • Pre clinical research
  • Drug review
  • Drug safety monitoring
  • AI for Drug repurposing to predict accurate drug-target interactions

Our AI Models for all stages of drug discovery

  • Traditional reinforcement learning
  • Generative Adversarial Networks (GANs)
  • Quantum Machine Learning (QML)
  • Molecular Docking Algorithms
  • Deep learning based drug-target interactions
  • Neural networks to screens millions of compounds against set targets

The benefits of AI enabled drug discovery

  • AI models can show accuracy of upto 95% in new molecule feasibility
  • Significant reduction in time for screening and molecule selection
  • 25%-30% reduction in costs for preclinical and clinical development
  • Accurate monitoring of drug interaction with target
  • Increased number of approved therapies and faster time to market for new drugs
  • Lower time to revenue for drug companies


If you would like to know more or discuss our use cases in detail