DeepCRE: Revolutionizing Drug R&D with Cutting-Edge Computational Models

• DOI: 10.48550/arxiv.2403.03768
• Publication Date: 2024-03-06
• Authors: Yushuai Wu
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; FOS: Biological sciences; Quantitative Biology - Quantitative Methods; Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)

The field of pharmaceutical development and therapeutic application both face substantial challenges. Therapeutic domain calls for more treatment alternatives while numerous promising pre-clinical drugs fail in clinical trails. One the reasons is inadequacy Cross-drug Response Evaluation (CRE) during late stage drug development. Although in-silico CRE models offer a solution to this problem, existing methodologies are either limited early stages or lack capacity comprehensive analysis. Herein, we introduce novel computational model named DeepCRE present potential advancing discovery outperforms best by achieving an average performance improvement 17.7\% patient-level CRE, 5-fold increase indication-level CRE. Furthermore, has identified six candidates that show significantly greater effectiveness than comparator set two approved 5/8 colorectal cancer (CRC) organoids. This highlights DeepCRE's ability identify collection with superior effects, underscoring its revolutionize