Make Accurate Drug Discovery with Reduced Operational Costs

The drug discovery process involves complex activities. With the help of AI (Artificial Intelligence) and ML (Machine Learning) you can accelerate the process while following the protocols.

Our team at i2e carries the expertise to understand your requirements and develop best-in-class custom software solutions which can streamline the drug discovery process. Explore the various applications of AI and ML in drug discovery.

1.Improve the success rate of new drugs

Using AI and ML-powered applications pharma companies can increase the success rates of drug discovery. From predicting success rates to estimating toxicities, AI can help you visualize the viability of the drug and gauge its success beforehand.

  • Precision drug targeting with AI-powered 3D cell structures.
  • Predict toxicity of environmental compounds.
  • Differentiate between primary, secondary and tertiary effects of molecules.
  • Pinpoint relationships between genes, symptoms, diseases, tissues, and drugs.
  • Effectively predict the binding affinity between drugs and target proteins.

2.Create affordable drugs

To make health care available to all, pharma companies can make use of AI and ML technologies, to increase precision during drug discovery. Thus, creating affordable drugs.

Automate repetitive tasks and save hundreds of man-hours.

Avoid trial and error and accurately select excipients.

Replace traditional animal testing with advanced AI models.

Cost effective drug repurposing through forecasting efficacy and drug interactions.

3.Reduce operational costs

AI and ML also help businesses to reduce operational costs, without compromising the quality of the drug development process.

  • Adopt cloud computing for operational efficiency, and scalability.
  • Accurate decision making with integrated framework and customized dashboards.
  • Accessibility to the entire product project life cycle along with real-time data .
  • Constant feedback for continuous improvement.

Pre-clinical research

Evaluate drug safety accurately by digitalization of the toxicity studies using AI and ML.

Toxicity studies

High toxicity is the major contributor to two-thirds of post-marketing drug withdrawals. Thanks to technologies backed by AI and ML, preclinical toxicity studies are now faster, cost-effective, and ethical.

Replicate drug safety parameters using Quantitative structure-activity relationship (QSARs) models.
Accurately predict the toxicity scores of drug molecules.
Predict toxicity from the images of cells using Convolutional Neural Networks (CNNs).

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i2e Consulting is a pharma focused IT service provider company
which is leading innovation in the industry.

Explore opportunities and gain competitive advantage.

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