As global research institutions increasingly explore the use of AI in candidate compound screening, Creative Biolabs has emerged as a preferred partner with its AI-assisted drug discovery platform.
SHIRLEY, NY, August 09, 2025 /24-7PressRelease/ — AI is making strides in reshaping the drug R&D process in an industry combating the triad of high expenditure, high failure rates, and long development timelines.
“We aim to transform AI from a lab-based tool into a true engine driving drug discovery strategies,” said the director of drug discovery at Creative Biolabs. “Especially in scenarios where target identification is complex and data dimensionality is high, traditional approaches fall short. In looking to improve the success rate and efficiency during the drug development process, AI is indispensable.”
One of the platform’s core strengths lies in its AI-driven high-throughput screening (HTS) analysis service. As a critical step in modern drug discovery workflows, HTS enables rapid evaluation of tens of thousands of compounds to identify potential actives. However, the process often generates overwhelming volumes of data, accompanied by high false positive and false negative rates, which can leave researchers drowning in uncertainty.
To resolve this issue, Creative Biolabs has integrated machine learning and deep learning technologies into its platform, applying structured processing and intelligent recognition to raw HTS datasets. The system not only reduces noise and artifacts but also builds predictive models for compound activity and mechanism of action (MoA). Furthermore, it integrates clinical and omics data to assess the potential applications of lead compounds across specific disease contexts.
“We’re not just identifying which molecules work—we’re understanding why they work. That’s where AI delivers its real value,” the director emphasized.
Beyond early-stage screening, Creative Biolabs has extended its AI capabilities into preclinical development. It has a particular focus on AI-driven biomarker identification. Predictive biomarkers differ from prognostic biomarkers, which describe the progression of disease, by aiming to classify which patient subgroups are most likely to respond to a given therapy.
Creative Biolabs leverages a contrastive learning-based neural network to extract key features from multi-omics and clinical data, enabling precise patient stratification while maintaining high model interpretability. The team places strong emphasis on transparency—ensuring that the output is not just a prediction but a traceable, verifiable decision-making pathway.
Moreover, the platform boasts powerful multi-modal data integration capabilities, capable of processing DNA/RNA expression data, proteomics, clinical metrics, and demographic information simultaneously. This provides a panoramic view for biomarker discovery. Combined with Creative Biolabs’ established wet-lab validation infrastructure, AI-identified biomarker candidates can rapidly proceed to experimental validation and iterative refinement—creating a true model-validation-optimization loop.
For more information, please visit https://ai.creative-biolabs.com/.
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With the support of artificial intelligence, Creative Biolabs is helping transition drug discovery from a game of chance to a discipline of deliberate design.
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