Machine Learning Approaches for Accelerating Drug Discovery and Reducing Clinical Trial Failures.
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866901105865129984 |
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| author | Sudhir, Siddharth |
| author_facet | Sudhir, Siddharth |
| contents | <p>This preprint reviews and synthesizes how modern machine learning is reshaping the drug discovery and clinical development pipeline, addressing two core bottlenecks in pharma R&D: extreme cost (often cited at multi-billion USD per approved drug) and high clinical trial failure rates. The paper surveys practical ML approaches across target identification, hit discovery, lead optimization, ADMET/toxicity prediction, de novo molecular design, and clinical trial risk modeling, highlighting where specific model families fit best, including graph neural networks for molecular property prediction and transformer-based architectures for molecule generation and sequence-driven tasks. </p> <p>A comparative evaluation is presented with reported gains such as improved hit identification performance, faster lead optimization cycles, stronger prediction of mid-stage (Phase II) trial failures, and robust toxicity prediction (e.g., AUC > 0.85) alongside generation of novel compounds with high synthetic accessibility. The manuscript also discusses real-world limitations: data quality, bias, interpretability, privacy/proprietary constraints, and regulatory acceptance, while outlining near-term and longer-term integration directions (e.g., federated learning, digital twins, automated labs, and quantum ML). </p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18052372 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Machine Learning Approaches for Accelerating Drug Discovery and Reducing Clinical Trial Failures. Sudhir, Siddharth Computer and information sciences Medical and health sciences Pharmaceutical sciences <p>This preprint reviews and synthesizes how modern machine learning is reshaping the drug discovery and clinical development pipeline, addressing two core bottlenecks in pharma R&D: extreme cost (often cited at multi-billion USD per approved drug) and high clinical trial failure rates. The paper surveys practical ML approaches across target identification, hit discovery, lead optimization, ADMET/toxicity prediction, de novo molecular design, and clinical trial risk modeling, highlighting where specific model families fit best, including graph neural networks for molecular property prediction and transformer-based architectures for molecule generation and sequence-driven tasks. </p> <p>A comparative evaluation is presented with reported gains such as improved hit identification performance, faster lead optimization cycles, stronger prediction of mid-stage (Phase II) trial failures, and robust toxicity prediction (e.g., AUC > 0.85) alongside generation of novel compounds with high synthetic accessibility. The manuscript also discusses real-world limitations: data quality, bias, interpretability, privacy/proprietary constraints, and regulatory acceptance, while outlining near-term and longer-term integration directions (e.g., federated learning, digital twins, automated labs, and quantum ML). </p> <p> </p> |
| title | Machine Learning Approaches for Accelerating Drug Discovery and Reducing Clinical Trial Failures. |
| topic | Computer and information sciences Medical and health sciences Pharmaceutical sciences |
| url | https://doi.org/10.5281/zenodo.18052372 |