Research Articles
Overcoming Domain Shift: A Practical Guide for Applying Catalyst Generative AI in Drug Discovery
Catalyst generative models promise to revolutionize molecular design, but their real-world application is hampered by domain shift—the performance gap between training data and target domains.
Overcoming Data Scarcity in Chemical AI: Advanced Strategies for Reaction-Conditioned Generative Models
This article provides a comprehensive guide for researchers and drug development professionals tackling the critical bottleneck of data scarcity in reaction-conditioned generative models for chemistry.
Advanced ASPEN PLUS Modeling of Catalytic Biomass Gasification: A Complete Guide for Sustainable Fuel Researchers
This comprehensive article provides a detailed guide to modeling catalytic biomass gasification using ASPEN PLUS software, tailored for researchers and scientists in sustainable energy and biofuel development.
Boosting Catalyst Prediction Accuracy: Advanced ANN Weight Optimization Strategies for Drug Discovery
This article explores cutting-edge Artificial Neural Network (ANN) weight optimization techniques for enhancing catalyst prediction in pharmaceutical research.
Artificial Neural Networks in Catalysis: Bridging Experimental Data and Theoretical Models for Accelerated Discovery
This article provides a comprehensive review of Artificial Neural Networks (ANNs) as transformative tools in catalysis research, addressing four key intents for a scientific audience.
Ensemble ANN Methods in Catalyst Performance Prediction: A Comprehensive Guide for Drug Development Research
This article provides a comprehensive analysis of Artificial Neural Network (ANN) ensemble methods for predicting catalyst performance in drug development.
Optimizing Catalytic Activity: ANN-Conjugated Polymer Urease Biosensors for Advanced Biomedical Sensing
This article provides a comprehensive analysis of artificial neural network (ANN)-conjugated polymer urease biosensors, focusing on their catalytic activity optimization for biomedical applications.
Decoding Chirality: How AI-Driven Descriptors are Revolutionizing Enantioselective Reaction Prediction
This article provides a comprehensive overview of conformation-independent molecular descriptors for Artificial Neural Networks (ANNs) in predicting enantioselective reaction outcomes.
Optimizing Oxidative Coupling of Methane with AI: A Comprehensive Guide to ANN-Based Ethylene and Ethane Yield Prediction
This article provides a detailed framework for researchers and chemical engineers developing artificial neural network (ANN) models to predict ethylene and ethane yields in the Oxidative Coupling of Methane (OCM)...
ANN Catalytic Activity Prediction: A Comprehensive Guide for Biomedical Researchers
This article provides a detailed exploration of Artificial Neural Networks (ANNs) for predicting catalytic activity, a critical task in drug discovery and enzyme engineering.