ISMB 2024: Streamlining drug development with conversational AI-powered knowledge graphs: From preclinical discovery to clinical trials

The drug development industry faces an efficacy crisis, with a 90% clinical trial failure rate, an average of 9 years, and $1.5 billion spent on bringing a new drug to market. This is largely due the complex process of clinically translating and validating drug-target cellular machinery within extensive scientific literature. To tackle this challenge, we applied conversational AI-powered knowledge graphs (KGs) to various aspects of the drug development process, from preclinical drug discovery and repurposing to matching patients with clinical trials. Our strategy includes: 1) creating FAIR (Findable, Accessible, Interoperable, and Reusable) knowledge graphs with ontology-validated Named Entity Recognition, 2) leveraging embeddings models and fine-tuned large language models (LLMs) for deciphering biomedical relationships, 3) identifying key therapeutic targets and adverse reactions using AI-driven graph analysis and 4) harnessing LLMs reasoning capabilities to match patients to clinical trials.

We showcase how conversational AI-powered KGs can enhance microbiome repurposing and streamline patient matching with high accuracy. Using benchmark datasets like BIOASQ, we assessed various embedding models for capturing both structural and semantic relationships among biomedical entities. We further illustrate how fine-tuning LLMs, such as BioGPT, with synthetic datasets can accurately improve their understanding of biomedical contexts, classify the regulatory effects of intermingled biological entities and assess patients' eligibility for clinical trials based on matching criteria. Expanding on these methods, we employ AI-driven graph analysis techniques (e.g., graph centrality metrics) to precisely identify key therapeutic and diagnostic targets for specific diseases and drugs, ultimately improving the overall success rate in developing innovative treatments.