Research Articles
Generative AI in Materials Science: A Systematic Review of Performance Metrics and Real-World Impact
This systematic review synthesizes the current landscape of performance metrics for generative artificial intelligence (GenAI) in materials science.
Beyond Novelty: A Framework for Assessing Diversity and Quality in AI-Generated Biomedical Materials
This article provides researchers, scientists, and drug development professionals with a comprehensive framework for evaluating the novelty and diversity of AI-generated materials.
Strategies for Optimizing Computational Efficiency in AI-Driven Materials Generation
This article addresses the critical challenge of computational efficiency in the AI-driven generation of novel materials, a pivotal concern for researchers and drug development professionals.
VAE vs. GAN for Materials Discovery: A Comparative Analysis for Accelerated Drug Development and Innovation
This article provides a comprehensive comparative analysis of two pivotal deep generative models—Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs)—in the context of materials discovery.
Beyond the Zeros: A Materials Scientist's Guide to Handling Zero-Inflation in Data Analysis
This article provides a comprehensive guide for researchers and drug development professionals facing the challenge of zero-inflated data in materials science and biomedical research.
Inverse Molecular Design Using Generative AI: A New Paradigm for Accelerated Drug and Material Discovery
This article provides a comprehensive overview of the transformative field of inverse molecular design powered by generative artificial intelligence (GenAI).
Reinforcement Learning for Molecular Design Optimization: Advanced Methods and Applications in Drug Discovery
This article provides a comprehensive exploration of Reinforcement Learning (RL) applications in molecular design optimization, a transformative approach in modern drug discovery.
Improving Molecular Validity in Generative AI: Strategies for Clinically Relevant Drug Design
Generative artificial intelligence holds transformative potential for accelerating drug discovery by designing novel molecular structures.
Multimodal Fusion for Materials Property Prediction: Integrating AI, Graphs, and Language Models for Drug Discovery
This article explores the transformative potential of multimodal fusion models in accelerating materials property prediction, a critical task for drug discovery and materials science.
Fine-Tuning Strategies for Materials Foundation Models: A Guide for Biomedical and Clinical Research
This article provides a comprehensive guide for researchers and drug development professionals on fine-tuning materials foundation models.