Machine Learning-Aided Small-Molecule Virtual Screening: Recent Advances and Future Perspectives
We comprehensively review how AI and machine learning are reshaping small-molecule virtual screening, spanning AI-driven scoring functions, efficient search algorithms, and the paradigm shift toward generative de novo molecular design.
Sep 1, 2026
Precision Grounding: Augmenting Large Language Models with Evidence-Based Databases for Trustworthy Genetic Variant Summarization
LLMs are prone to generating inaccurate or misleading interpretation of genetic variants. To solve this, we propose a “precision grounding” approach that enhances LLM with publicly available evidence-based databases and resources identified by domain experts.
Jul 1, 2026
Interactive active learning for literature screening: finetuning GPT with DeepSeek reasoning for cross-domain generalization
We propose an active learning framework that leverages disagreement between large language models (GPT and DeepSeek) to selectively fine-tune GPT models with reasoning-augmented supervision, significantly improving accuracy and recall in automated biomedical literature screening.
Mar 9, 2026
Testing and evaluation of generative large language models in electronic health record applications: a systematic review
Our systematic review shows that current evaluations of LLMs in real clinical settings are surprisingly narrow and mostly focused on radiology and decision support, while key areas like patient communication remain largely under-explored.
Jan 13, 2026
AI-assisted literature screening: A hybrid approach using large language models and retrieval-augmented generation
This study aims to improve literature screening efficiency and accuracy by developing an LLM-based approach that incorporates rule-based preprocessing, prompt engineering (including RAG), and ensemble techniques.
Nov 30, 2025
Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review
We conduct a comprehensive review of recent studies that leverage generative LLMs for EHR analysis and applications, focusing on their performance and strategies for improvement.
Aug 25, 2025
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning
We propose an asymmetric contrastive multimodal learning framework, an effective and training-efficient framework tailored for molecules, promoting cross-modality understanding between the molecular graph and other chemical modalities.
Jul 23, 2025
Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases
This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.
Jun 11, 2024
A machine learning approach to robustly determine director fields and analyze defects in active nematics
We developed a machine learning model for extracting reliable director fields from raw experimental images, which enables accurate analysis of topological defects.
Jan 31, 2024
Motif-based graph representation learning with application to chemical molecules
We designed a novel convolution module for graph representational learning on molecules with an efficient pretraining strategy, enabling the capture of local structural and semantic information from graph motifs.
Jan 11, 2023