Research Articles
Navigating the Vast Chemical Space: How Machine Learning is Revolutionizing Drug Discovery
The exploration of chemical space, estimated to contain over 10^60 drug-like molecules, represents a monumental challenge and opportunity for modern drug discovery.
Active Learning in Computational Chemistry: Accelerating Drug Discovery with AI
This article provides a comprehensive overview of active learning (AL), a transformative machine learning paradigm that is reshaping computational chemistry and drug discovery.
Active Learning for Virtual Screening: A Comprehensive Guide to Accelerating Drug Discovery
This article provides a comprehensive overview of the foundations and applications of active learning (AL) in structure-based virtual screening (VS) for drug discovery.
Low-Data Drug Discovery: Overcoming Data Scarcity with Active Deep Learning
This article explores the transformative potential of active deep learning (DL) in accelerating drug discovery within data-scarce environments.
Chemical Space Exploration with Active Learning and Alchemical Free Energies: A New Paradigm for Accelerated Drug Discovery
This article explores the powerful synergy between active learning (AL) and alchemical free energy calculations (AFEC) for navigating vast chemical spaces in drug discovery.
Active Learning in Drug Discovery: A Guide to Accelerating AI-Driven Therapeutics
This article provides a comprehensive introduction to Active Learning (AL) and its transformative role in modern drug discovery.
Active Learning for Chemical Space Exploration: Strategies, Applications, and Future Directions in Drug Discovery
This article provides a comprehensive overview of active learning (AL) methodologies for navigating vast chemical spaces in drug discovery and materials science.
Benchmarking Few-Shot Learning for Molecular Property Prediction: A Comprehensive Guide for Drug Discovery
This article provides a systematic benchmark and comprehensive analysis of few-shot learning (FSL) approaches for molecular property prediction, a critical capability in early-stage drug discovery and materials design where labeled...
Traditional vs. Deep Learning for Molecular Property Prediction: A Comprehensive Comparison for Drug Discovery
This article provides a systematic comparison of traditional machine learning and modern deep learning methods for molecular property prediction, a critical task in drug discovery and materials science.
Beyond Accuracy: A Practical Framework for Assessing Model Performance on Molecular Activity Cliffs
This article provides a comprehensive guide for researchers and drug development professionals on evaluating machine learning model performance in the presence of molecular activity cliffs—critical yet challenging phenomena in drug...