CAMBRIDGE, Mass. April 16, 2026 — Insilico Medicine, a clinical-stage biotechnology company powered by generative artificial intelligence (AI), today announced advancements to its unified AI framework for drug target discovery, integrating its previously introduced Target Identification Pro (TargetPro) and Target Identification Benchmark (TargetBench 1.0) into a validated system designed to improve the accuracy, reliability, and scalability of early-stage drug development.
Building on its research published in Scientific Reports (2026), the framework now demonstrates strong performance across benchmarking and real-world discovery workflows, reinforcing its role as a standardized foundation for AI-driven target identification.
Drug discovery has long been constrained by high failure rates, with nearly 90% of clinical candidates failing—often due to weak or poorly validated biological targets. Insilico’s integrated TargetPro–TargetBench framework addresses this challenge by combining disease-specific predictive modeling with rigorous, standardized evaluation.
Building on its research published in Scientific Reports (2026), the framework now demonstrates strong performance across benchmarking and real-world discovery workflows, reinforcing its role as a standardized foundation for AI-driven target identification.
Drug discovery has long been constrained by high failure rates, with nearly 90% of clinical candidates failing—often due to weak or poorly validated biological targets. Insilico’s integrated TargetPro–TargetBench framework addresses this challenge by combining disease-specific predictive modeling with rigorous, standardized evaluation.
A New Standard for AI-Driven Target Discovery
TargetPro is a disease-specific machine learning model spanning 38 diseases across oncology, metabolic, immune-related, fibrotic, and neurological categories. The model integrates 22 distinct omics and text-based scores from Insilico’s AI target discovery platform, PandaOmics, to identify targets with a high likelihood of clinical success. In performance evaluations, TargetPro substantially outperformed individual omics and text-based approaches, demonstrating the power of multimodal data integration. TargetPro also revealed that some data types or analytic models were more predictive in the context of different diseases, underscoring the value of disease-specific target identification models.
TargetBench 1.0: Establishing a Benchmarking Standard
Complementing TargetPro, TargetBench 1.0 provides a comprehensive benchmarking system for evaluating target discovery models, including large language models (LLMs). There has been a lack of evaluation methods to provide an unbiased comparison of these models, as each relies on different data types and sources and optimizes for different result characteristics. To fill in this “benchmarking gap” and evaluate models for capturing both confidence and novelty, the platform assesses performance based on the ability to recover known clinical targets and prioritize high-quality novel candidates.
TargetPro demonstrated an overall precision-at-top-K of 71.6% in retrieving known clinical targets across all disease categories, representing a 1.7 to 5.5-fold improvement over leading LLM-based approaches. Together, TargetPro and TargetBench address a critical industry gap: not only generating high-quality targets, but systematically validating them against standardized, reproducible benchmarks.
TargetPro demonstrated an overall precision-at-top-K of 71.6% in retrieving known clinical targets across all disease categories, representing a 1.7 to 5.5-fold improvement over leading LLM-based approaches. Together, TargetPro and TargetBench address a critical industry gap: not only generating high-quality targets, but systematically validating them against standardized, reproducible benchmarks.
Driving Actionable, Translationally Novel Targets
Beyond predictive performance, the integrated framework emphasizes translational readiness—ensuring that nominated targets are actionable in downstream experimental and clinical workflows. Among TargetPro’s predicted novel candidates:
These results demonstrate the system’s ability to generate actionable intelligence—prioritized, biologically grounded targets with strong potential for successful development.
- 95.7% have available 3D protein crystal structures, enabling downstream drug design
- 86.5% are classified as druggable with supporting clinical evidence
- 46% are associated with approved drugs in other indications, highlighting repurposing opportunities
These results demonstrate the system’s ability to generate actionable intelligence—prioritized, biologically grounded targets with strong potential for successful development.
Toward Scalable and Standardized AI in Drug Discovery
The TargetPro–TargetBench framework represents a critical step toward more scalable and standardized AI-driven drug discovery. By integrating predictive modeling with rigorous benchmarking, Insilico is helping establish a common framework for evaluating and deploying AI systems across pharmaceutical R&D. The latest versions, TargetPro 2.0 and TargetBench 2.0, expand disease coverage from 38 to 100 indications across 10 therapeutic areas, including new coverage of cardiovascular, ophthalmological, reproductive, and mental health disorders. TargetBench 2.0 also introduces additional benchmarking dimensions, such as mechanism-of-action clarity, commercial potential and average precision, to further strengthen the platform's utility for drug discovery decision-making.
The approach also aligns with Insilico’s broader efforts to advance reliable scientific AI, emphasizing the importance of standardized evaluation to accelerate adoption and improve outcomes across the drug discovery pipeline.
“Most failures in drug discovery begin with weak target selection,” said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine. “By combining disease-specific modeling with rigorous benchmarking, we are moving from predictive AI to actionable intelligence—enabling scientists to prioritize targets with greater confidence and speed. This framework brings us closer to more reliable, scalable, and efficient therapeutic discovery.”
The approach also aligns with Insilico’s broader efforts to advance reliable scientific AI, emphasizing the importance of standardized evaluation to accelerate adoption and improve outcomes across the drug discovery pipeline.
“Most failures in drug discovery begin with weak target selection,” said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine. “By combining disease-specific modeling with rigorous benchmarking, we are moving from predictive AI to actionable intelligence—enabling scientists to prioritize targets with greater confidence and speed. This framework brings us closer to more reliable, scalable, and efficient therapeutic discovery.”