Identify drug targets

Start by clearly defining the disease of interest, its pathophysiology, and known molecular pathways. Use AI-powered literature mining tools (e.g., PubTator, Semantic Scholar) to extract key genes, proteins, and pathways associated with the disease. Formulate a biological hypothesis about which types of targets (e.g., enzymes, receptors, transcription factors) are most likely to be druggable and disease-modifying.

Why Causaly: Causaly specializes in AI-driven literature mining and pathway analysis for deciphering disease pathophysiology, directly matching the need for defining disease context and biological hypothesis.

Leverage AI models (e.g., deep learning on multi-omics data, AlphaFold for protein structure) to identify novel genes or proteins that are differentially expressed or mutated in the disease. Use platforms like Open Targets or TargetMine to integrate genetic associations, expression data, and protein interaction networks. Rank candidates by confidence scores from predictive models.

Why BenevolentAI (The Benevolent Platform™): BenevolentAI's platform is designed for novel biological target identification using AI-driven multi-omics analysis, fitting the need for genomic and proteomic discovery.

Cross-reference candidate targets against real-world patient data sources such as electronic health records (EHRs), claims databases, and biobanks. Use natural language processing (NLP) to extract clinical associations (e.g., comorbidity patterns, treatment outcomes) from unstructured clinical notes. Apply statistical models to confirm that target modulation correlates with disease severity or progression in human populations.

Why Tempus: Tempus provides genomic profiling and clinical trial matching, directly supporting validation with real-world evidence and clinical data.

Use AI summarization tools (e.g., GPT-4, SciSpace, Elicit) to generate concise summaries of the top 5-10 validated targets. For each target, extract key findings from recent publications, including mechanistic studies, animal models, and clinical trials. Organize summaries into a structured report with evidence tables and confidence ratings.

Why Elicit: Elicit is designed for automated literature review and structured data extraction from PDFs, perfectly matching the need for AI-assisted literature summarization.

Use AI-based safety prediction tools (e.g., DeepTox, ProTox-II) to evaluate potential toxicity and off-target interactions of the top candidates. Cross-reference with known adverse event databases (e.g., FAERS, SIDER) to identify safety signals. This step is optional but recommended before committing to experimental validation.

Why Ersilia Open Source Initiative: Ersilia Open Source Initiative predicts toxicity and ADME properties of compounds, directly addressing target safety and off-target risk assessment.