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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.

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