7th Edition of
Chemistry World Conference
June 21-23, 2027 | 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.
Brian Blagg
University of Notre Dame, United States
Yong Xiao Wang
Albany Medical College, United States
Thomas J Webster
Brown University, United States
Klaus Radunsky
National Standardization International, Austria
Siegmund Reissmann
Friedrich Schiller University, Germany
Alessia Corami
Aix-Marseille University, Italy
Guy Patchronik
Ariel University, IsraelSubmit your abstract Today
Title : Fighting antibiotic resistance: Eliminating implant infections in humans without antibiotics
Thomas J Webster, Brown University, United States
Title : Novel polymer-mediated therapeutic options for chronic obstructive pulmonary disease
Yong Xiao Wang, Albany Medical College, United States
Title : Turning problems into opportunities via small molecule design
Brian Blagg, University of Notre Dame, United States
Title : Durable surface-confined metal-terpyridine architectures for electrochromic applications
Olena Zenkina, Ontario Tech University, Canada
Title : Advances in plasma-based waste treatment
Hossam A Gabbar, Ontario Tech University, Canada
Title : Application of the Michaelis-Menten formalism to hydrocracking-related reactions and analysis of the features of the reactors
Orchidea Maria Lecian, Sapienza University of Rome, Italy