LTM Data And AI DeepCure

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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.

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LTIMindtree Limited

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