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LTM Data And AI DeepCure
https://catalogartifact.azureedge.net/publicartifacts/ltim.ltm_data_and_ai_deepcure-5a0913fa-9a8f-48ac-8622-919c547b8b1c/image1_LTM216X216px.png

LTM Data And AI DeepCure

LTIMindtree Limited

การกำหนดราคา ฟรี

LTM’s DeepCure AI accelerates drug discovery using Agentic AI by integrating literature review, molecular screening, analytics, and decision intelligence into a unified workflow.

Business Challenges:

  • Drug discovery is a long and expensive process, typically taking 10–15 years, with nearly 90% of clinical trial candidates failing before approval.
  • Biomedical research and drug discovery workflows are fragmented, time-intensive, and heavily dependent on manual effort.
  • Researchers must analyze vast volumes of scientific literature, chemical libraries, and experimental data to identify potential drug candidates.
  • Manual literature review across publications, patents, and clinical datasets leads to delays and increased cognitive overload.
  • Screening large molecular datasets is computationally intensive and limited by existing tools and human intuition.
  • These challenges delay the identification of high-confidence candidates and slow down R&D decision-making.

    • Solution Approach:

  • Built on Microsoft Azure OpenAI models and Azure Foundry, LTM’s DeepCure AI is an agentic AI–driven drug discovery platform designed to accelerate biomedical research workflows.
  • It leverages multiple ReACT-based AI agents to reason, plan, and execute tasks across the drug discovery lifecycle.
  • These agents are powered by domain-specific knowledge sources such as PubChem, PubMed Central, and ChEMBL to provide scientifically grounded insights.
  • The platform integrates literature review, molecular screening, conformer analysis, and decision intelligence into a unified end-to-end workflow.
  • It enables deep search capabilities by refining queries and retrieving, summarizing, and validating findings across multiple scientific publications.
  • A LLM-as-a-judge framework evaluates outputs based on consistency, completeness, relevance, and risk of bias to ensure reliable and trustworthy insights.
  • Key capabilities include AI-driven molecular screening to identify high-potential compounds, conformer analysis for evaluating molecular structure and stability, and advanced visualization for exploring 2D/3D molecular structures.
  • The platform also provides guided analytics and decision intelligence to support data-driven decision-making across the drug discovery lifecycle.

    • Benefits:

  • Accelerates drug discovery timelines by reducing literature review and molecule screening efforts from weeks to hours, enabling 30–40% faster time-to-candidate.
  • Improves candidate selection and decision-making by narrowing millions of compounds to a focused set of high-confidence candidates using integrated scientific insights.
  • Supports compression of the drug discovery lifecycle by automating key research workflows.
  • Reduces manual effort and cognitive burden by automating repetitive research tasks.
  • Provides a scalable and extensible solution to support future advancements in AI-driven drug discovery.

    • สรุปย่อ

    https://catalogartifact.azureedge.net/publicartifacts/ltim.ltm_data_and_ai_deepcure-5a0913fa-9a8f-48ac-8622-919c547b8b1c/image3_DeepCure.png