5th Edition of
Chemistry World Conference
June 02-04, 2025 | Rome, Italy
AI & Machine Learning in Chemistry
Advancements in AI & machine learning in chemistry are revolutionizing research by enhancing reaction predictions, material design, and drug discovery. Machine learning models analyze vast chemical datasets, accelerating compound screening and reaction optimization. AI-driven simulations reduce trial-and-error in experiments, making chemical processes more efficient and sustainable. Neural networks and predictive algorithms improve molecular modeling, enabling precise identification of novel compounds. Automation in data analysis streamlines workflows in spectroscopy, chromatography, and synthesis. The integration of AI with experimental chemistry fosters innovation across multiple fields, from pharmaceuticals to green chemistry. As computational power grows, machine learning continues to reshape chemical research, driving faster discoveries and smarter, data-driven solutions.
Yong Xiao Wang
Albany Medical College, United States
Hossam A Gabbar
Ontario Tech University, Canada
Stanislaw Dzwigaj
Sorbonne Universite, France
Haibo Ge
Texas Tech University, United States
Thomas J Webster
Hebei University of Technology, China
Makarov Vladimir
Institute of Permafrost Science, Russian Federation
Silvia Elizabeth Asis
Universidad de Buenos Aires, ArgentinaSubmit your abstract Today
Title : Advances in plasma-based waste treatment for sustainable communities
Hossam A Gabbar, Ontario Tech University, Canada
Title : Nanostructured biodevices based on carbon nanotubes and glyconanoparticles for bioelectrocatalytic applications
Serge Cosnier, Silesian University of Technology, Poland
Title : Carbon capture and storage: The impact of impurities in CO2 streams
Andy Brown, Progressive Energy Ltd, United Kingdom
Title : Supramolecular nano chemistries: Fighting viruses, inhibiting bacteria and growing tissues
Thomas J Webster, Hebei University of Technology, China
Title : Chemical engineering of vanadium and tantalum zeolites for application in environmental catalysis
Stanislaw Dzwigaj, Sorbonne Universite, France
Title : Disrupting TNF-α and TNFR1 interaction: Computational insights into the potential of D-Pinitol as an anti-inflammatory therapeutic
Ferran Acuna Pares, Universidad Internacional de la Rioja (UNIR), Spain