Research Articles
Benchmarking Machine Learning Architectures for Molecular Synthesizability: A New Paradigm for Drug Discovery
The critical challenge in computational drug discovery is the generation of molecules with optimal pharmacological properties that are also synthesizable in the laboratory.
Beyond Thermodynamics: Evaluating Next-Gen AI for Predicting Synthesis of Complex Crystal Structures
Accurately predicting which computationally designed crystal structures can be experimentally synthesized is a critical bottleneck in materials discovery, particularly for complex systems relevant to pharmaceutical development.
AI-Driven Precursor Selection for Complex Inorganic Compounds: From Foundational Principles to Advanced Optimization
Selecting optimal precursors is a critical yet challenging step in the synthesis of complex inorganic materials, directly impacting the success and efficiency of research in areas ranging from battery technology...
Beyond the Training Set: Strategies for Generalizing Synthesizability Models to Novel Material Classes
This article addresses the critical challenge of generalizing AI-based synthesizability models beyond their training data to accelerate the discovery of new materials and drug candidates.
Beyond Energy: AI and Data-Driven Strategies for Predicting Synthesizability Beyond Thermodynamic Limits
For researchers and drug development professionals, accurately predicting whether a theoretically designed material or molecule can be synthesized remains a formidable challenge.
Extracting Synthesis Procedures with Natural Language Processing: A Guide for Drug Development and Biomedical Research
This article provides a comprehensive overview of Natural Language Processing (NLP) methodologies for the automated extraction of synthesis procedures from unstructured text.
Bridging Theory and Lab: AI-Driven Methods for Predicting Synthesis of Novel Crystal Structures
The acceleration of computational materials discovery has created a pressing challenge: determining which theoretically predicted crystal structures can be successfully synthesized in the laboratory.
Computational Prediction of Solid-State Reactions: From Fundamentals to AI-Driven Synthesis
This article provides a comprehensive review of computational methods accelerating the prediction and optimization of solid-state reaction synthesis.
How Deep Learning Models Learn Chemical Principles for Synthesizability
This article explores how deep learning models are trained to understand and predict the synthesizability of chemical compounds, a critical challenge in drug discovery and materials science.
CSLLM: How the Crystal Synthesis Large Language Model is Revolutionizing Drug Discovery and Materials Design
The Crystal Synthesis Large Language Model (CSLLM) framework represents a groundbreaking shift in predicting material synthesizability, a critical bottleneck in drug development and materials science.