Research Articles
Learning Medicinal Chemistry Intuition: How AI Predicts Expert Evaluations in Drug Discovery
This article explores the emerging field of computational prediction for medicinal chemist evaluations, a critical bottleneck in drug discovery.
Parallel Tempering Monte Carlo: Advanced Sampling for Complex Thermodynamic Properties in Drug Discovery
This article provides a comprehensive examination of Parallel Tempering (PT) Monte Carlo, a powerful enhanced sampling technique crucial for calculating thermodynamic properties in complex molecular systems.
Active Learning Strategies for Data-Scarce Chemical Problems: A Guide for Efficient Discovery
This article provides a comprehensive guide for researchers and drug development professionals on leveraging active learning (AL) to overcome data scarcity in chemical and materials science.
Basin-Hopping Method: A Complete Guide to Global Optimization for Drug Discovery and Biomolecular Structure Prediction
This article provides a comprehensive exploration of the Basin-Hopping (BH) algorithm, a powerful global optimization technique essential for navigating complex potential energy surfaces in computational chemistry and drug discovery.
Particle Swarm Optimization for Carbon Cluster Structures: From Theory to Biomedical Applications
This article explores the application of Particle Swarm Optimization (PSO) for determining the stable structures of carbon clusters (Cn).
Adam Optimization in Deep Learning: Accelerating Drug Discovery and Molecular Design
This comprehensive review explores the transformative role of the Adam (Adaptive Moment Estimation) optimizer in deep learning applications for chemistry and drug discovery.
Global Minimum Search of Molecular Clusters Using Genetic Algorithms: Principles, Optimization, and Applications in Drug Discovery
This article provides a comprehensive overview of genetic algorithms (GAs) for locating the global minimum energy structures of molecular clusters, a critical task in computational chemistry and drug discovery.
Optimizing Molecular Property Prediction: A Guide to Stochastic Gradient Descent in Drug Discovery
Molecular property prediction is a cornerstone of modern drug discovery, yet it is frequently hampered by scarce and expensive experimental data.
Bayesian Optimization for Chemical Hyperparameter Tuning: A Machine Learning Framework for Accelerated Drug Discovery
This article provides a comprehensive guide to Bayesian Optimization (BO), a powerful machine learning strategy for efficiently tuning hyperparameters in chemical and drug discovery applications.
Optimization Algorithms for Potential Energy Surface Mapping: From Machine Learning Force Fields to Drug Discovery Applications
Accurate and efficient mapping of Potential Energy Surfaces (PES) is fundamental to computational chemistry and drug discovery, enabling the prediction of molecular properties, reaction pathways, and catalytic processes.