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<p>Target identification is crucial for drug development. AI-driven approaches leveraging multi-omics and computational modeling can accelerate this process. However, integrating multi-modal data for disease-specific target identification and predicting translational potential remains challenging. Moreover, the absence of a systematic evaluation framework for model performance limits confidence in target reliability. This study presents a unified framework combining machine learning-based target identification with comprehensive benchmarking. We first developed Target Identification Pro (TargetPro), a disease-specific model spanning 38 diseases across oncology, metabolic, immune, fibrotic, and neurological categories. TargetPro shows strong predictive performance for clinical-stage targets and reveals disease-specific patterns, underscoring the need for tailored target detection models. We next created Target Identification Benchmark (TargetBench 1.0) to assess target identification systems, including large language models, based on their ability to recover established targets and find high-quality novel candidates. This integrated approach offers a streamlined strategy to evaluate target discovery models, ultimately improving drug development efficiency.</p>

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Advancing target discovery through disease-specific integration of multi-modal target identification models and comprehensive benchmarking system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月12日

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  • Howell Leung,
  • Chengchen Duan,
  • Wenhao Gou,
  • Jianjiu Chen,
  • Ying Xin,
  • Zetian Zheng,
  • Vladimir Naumov,
  • David Gennert,
  • Man Zhang,
  • Alex Aliper,
  • Feng Ren,
  • Evgeny Izumchenko,
  • Frank W. Pun,
  • Alex Zhavoronkov

摘要

Target identification is crucial for drug development. AI-driven approaches leveraging multi-omics and computational modeling can accelerate this process. However, integrating multi-modal data for disease-specific target identification and predicting translational potential remains challenging. Moreover, the absence of a systematic evaluation framework for model performance limits confidence in target reliability. This study presents a unified framework combining machine learning-based target identification with comprehensive benchmarking. We first developed Target Identification Pro (TargetPro), a disease-specific model spanning 38 diseases across oncology, metabolic, immune, fibrotic, and neurological categories. TargetPro shows strong predictive performance for clinical-stage targets and reveals disease-specific patterns, underscoring the need for tailored target detection models. We next created Target Identification Benchmark (TargetBench 1.0) to assess target identification systems, including large language models, based on their ability to recover established targets and find high-quality novel candidates. This integrated approach offers a streamlined strategy to evaluate target discovery models, ultimately improving drug development efficiency.