AI-Driven Drug Discovery & Development

Accelerate the critical stages of target validation, hit identification, lead optimization, and preclinical development with fewer false positives.

The Modern Approach to R&D in Pharma

Taking a digital-first approach has become a strategic imperative in the pharmaceutical and biopharma industries. Companies winning first mover advantage are putting tremendous investments in advanced technology in R&D, including artificial intelligence (AI), to ramp up their pace of discovery.

Yet, many companies are still entrenched in traditional approaches, along with the accompanying challenges. They continue to rely on research intuition rather than data and depend on subjective decision-making by a few experts, which could be automated and made more precise. Valuable researchers spend their time on mundane tasks, rather than solving complex problems and innovating. They operate within the status quo of repetitive analysis and process friction.

Enthought | AI in Pharma

Enthought | AI in Drug Discovery and Development

Solutions That Accelerate R&D In Critical Early Stages

Enthought helps pharmaceutical companies and CROs leverage AI and machine learning to accelerate drug discovery and development to advance their unique value proposition and competitive advantage. Our transformative digital solutions are tailored for your unique IP and built to augment and fill in the gaps of other software and tools. Enthought develops solutions to:

  • Automate workflows and analyses
  • Enhance data reliability and reduce false positives
  • Operationalize and scale researcher intuition
  • Amplify data-driven decision-making
  • Enable friction-less collaborations

Accelerate Your Path to Clinical Trials

Enthought | AI in Preclinical Drug Discovery and Development

A mid-size cancer therapeutics biotechnology company came to Enthought looking for a solution to their experiment data challenges. Their proprietary High Throughput Screening platform hardware and data capture had recently been operationalized, but analysis of the large amount of data required aggregating information across thousands of files. The bottlenecks and inefficiencies in their analysis pipeline were hindering meaningful progress and lengthening time to discovery. Read this case study to learn how we enabled their researchers to more quickly pursue potential targets using the power of AI and machine learning.

Let's Talk About AI in Your Lab

Learn more about how Enthought can help advance and accelerate the work of your R&D organization.

Industry Leaders Innovate with Enthought