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Highlights ● Insilico published a new study unveiling Target Identification Pro (TargetPro), a superior disease-specific model, and TargetBench 1.0, the first standardized benchmarking framework for target discovery. ● TargetPro achieved 71.6% retrieval of known clinical targets, a 2–3x improvement over large language models (LLMs) such as GPT-4o, Grok3, DeepSeek-R1, Claude-Opus-4, BioGPT, and public platforms like Open Targets. ● TargetPro’s predicted novel targets demonstrated 95.7% structure availability, 86.5% druggability, and 46% repurposing potential, outperforming competing systems on all measures. ● TargetBench’s explainable AI models revealed disease-specific feature importance patterns, emphasizing the value of disease-specific target identification models. ● New gold standard approaches improve accuracy, reliability, and transparency in AI-driven drug discovery.

The alleged deaths were detected by the FDA’s Vaccine Adverse Event Reporting System, reports from which “generally cannot be used to determine” causation or even contribution, according to the agency.