Mehak Gopal | Searcharxiv

arXiv2026

Machine learning-based modeling to predict inhibitors for targets of Alzheimer's Disease

Alzheimer's Disease is a chronic neurodegenerative disorder projected to affect 115 million people by 2050, driven by mechanisms like the cholinergic and amyloid hypotheses and insulin signaling disruptions involving key targets such as BACE-1, AChE, and GSK-3 beta. Utilizing machine learning (ML), we developed predictive models for inhibitor screening, achieving AUC-ROC scores above 0.9 for all targets. BACE-1 models showed high accuracy (86.63%) but limited chemical diversity. AChE models exhibited greater chemical diversity and similar performance (AUC-ROC: 92.86%, Accuracy: 85.20%), while GSK-3 beta models achieved an AUC-ROC of 91.14% with the highest proportion of viable drug candidates. These findings highlight the potential of ML in Alzheimer's drug discovery, with AChE and GSK-3 beta emerging as promising targets.

q-bio.QM↗

arXiv2025

Benchmarking LLMs for Predictive Applications in the Intensive Care Units

With the advent of LLMs, various tasks across the natural language processing domain have been transformed. However, their application in predictive tasks remains less researched. This study compares large language models, including GatorTron-Base (trained on clinical data), Llama 8B, and Mistral 7B, against models like BioBERT, DocBERT, BioClinicalBERT, Word2Vec, and Doc2Vec, setting benchmarks for predicting Shock in critically ill patients. Timely prediction of shock can enable early interventions, thus improving patient outcomes. Text data from 17,294 ICU stays of patients in the MIMIC III database were scored for length of stay > 24 hours and shock index (SI) > 0.7 to yield 355 and 87 patients with normal and abnormal SI-index, respectively. Both focal and cross-entropy losses were used during finetuning to address class imbalances. Our findings indicate that while GatorTron Base achieved the highest weighted recall of 80.5%, the overall performance metrics were comparable between SLMs and LLMs. This suggests that LLMs are not inherently superior to SLMs in predicting future clinical events despite their strong performance on text-based tasks. To achieve meaningful clinical outcomes, future efforts in training LLMs should prioritize developing models capable of predicting clinical trajectories rather than focusing on simpler tasks such as named entity recognition or phenotyping.

cs.AI↗