A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

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36. From Prompt to Drug: Toward Pharmaceutical Superintelligence
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38. Knowledge Graphs, Clinical Trials, Dataspace, and AI: Uniting for Progressive Healthcare Innovation
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40. Revolutionizing Pharmaceutical Industry: The Radical Impact of Artificial Intelligence and Machine Learning
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41. Computer‐Aided Drug Design
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42. GMPP-NN: a deep learning architecture for graph molecular property prediction
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43. High-throughput screening identifies ibuprofen as an sEV PD-L1 inhibitor for synergistic cancer immunotherapy
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45. Artificial Intelligence in Drug Discovery and Development: Transforming Pharmaceutical Innovation
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47. Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design
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48. Deriving general structure–activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods
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49. Flourishing and job satisfaction in employees working in UK clinical trial units: a national cross-sectional survey
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56. CTP-LLM: Clinical Trial Phase Transition Prediction Using Large Language Models
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