This article provides a comprehensive overview of data-driven methodologies revolutionizing the discovery and synthesis of novel materials.
This article explores the transformative impact of Artificial Intelligence (AI) on materials discovery, a critical frontier for advancements in medicine and technology.
This article provides a comprehensive guide for researchers and drug development professionals on validating the physical plausibility of computationally generated molecular structures.
This article provides a comprehensive performance comparison of transformer-based generative models for molecular design, a key technology in modern drug discovery.
This article provides a comprehensive analysis of the current state and critical challenges in benchmarking generative artificial intelligence models for molecular design.
This article explores the critical challenge of handling polymorph representation within generative AI models for material science.
This article provides a comprehensive guide for researchers and drug development professionals on overcoming the pervasive challenge of training instability in Generative Adversarial Networks (GANs).
This article provides a comprehensive analysis of mode collapse, a critical failure in generative AI models where output diversity severely degrades, hindering the discovery of novel materials and drugs.
This article addresses the critical challenge of data scarcity that constrains the development of robust generative AI models in materials science and drug discovery.
The integration of three-dimensional molecular representations into generative artificial intelligence is revolutionizing computational drug discovery.