Research Articles
Beyond the Plateau: Uncovering the Critical Limitations of Classical Scoring Functions in Binding Affinity Prediction
Accurate prediction of protein-ligand binding affinity is a cornerstone of computer-aided drug discovery, yet the performance of classical scoring functions has remained stagnant.
Boosting Binding Affinity Prediction: How Transfer Learning from Language Models is Revolutionizing Drug Discovery
Accurate prediction of drug-target binding affinity is a critical yet challenging task in computational drug discovery, traditionally hampered by limited labeled data and poor generalization.
The Physical Forces of Drug Action: Decoding Protein-Ligand Interactions from Fundamentals to AI
This article provides a comprehensive analysis of the physical interactions governing protein-ligand binding, a cornerstone of modern drug discovery.
Beyond the Benchmark: Tackling Data Bias to Build Generalizable Affinity Prediction Models
Accurate prediction of drug-target binding affinity is crucial for computational drug discovery, yet the generalization capability of many deep learning models has been severely overestimated due to pervasive data bias.
Scoring Functions in Virtual Screening: A Comprehensive Guide for Drug Discovery
Scoring functions are a critical, yet challenging, component of structure-based virtual screening (SBVS), directly impacting the success of modern drug discovery.
Structure-Based Drug Design: Foundations, Methods, and Future Directions in Computational Drug Discovery
This article provides a comprehensive overview of the foundations of Structure-Based Drug Design (SBDD), a pivotal computational approach in modern drug discovery.
Graph Neural Networks for Protein-Ligand Interactions: From Foundations to Clinical Applications
This article provides a comprehensive exploration of Graph Neural Networks (GNNs) and their transformative role in predicting protein-ligand interactions, a critical task in modern drug discovery.
How Attention Mechanisms Revolutionize Binding Affinity Prediction in Drug Discovery
This article explores the transformative role of attention mechanisms in computational models for predicting drug-target binding affinity (DTA), a critical task in modern drug discovery.
Binding Affinity Prediction: The AI Revolution in Drug Discovery
Accurate prediction of drug-target binding affinity is a cornerstone of modern computational drug discovery, enabling the rapid identification and optimization of therapeutic candidates.
Deep Learning for Protein-Ligand Binding Affinity Prediction: A Comprehensive Guide for Drug Discovery
The prediction of protein-ligand binding affinity (PLA) is a cornerstone of modern drug discovery, crucial for identifying and optimizing potential therapeutic compounds.