AI, Machine Learning, and Computational Tools for Greener Chemistry Outcomes
Organizers: Jun Li, Senior Principal Scientist, Bristol Myers Squibb, New Brunswick, NJ, USA; Jared Piper, Director, Pfizer, Groton, CT, USA
Finding the best sequence of reactions in the synthetic route design or the most promising catalyst in a wide array of organic transformations, are key endeavors exemplifying green chemistry principles to ultimately maximize synthetic efficiency, atom economy and minimize waste production. With the advent of artificial intelligence (AI), machine learning, and predictive analytics at large, along with development of powerful algorithms in the computational quantum chemistry from first principles, progresses have been made in multiple fronts in synthetic route design and reaction science.
In route design area, both AI/machine learning method and heuristic rule-based expert system are advancing the field rapidly. Simultaneously, predictive analytics methodology using green chemistry metrics has been incorporated in the route selection process to instill sustainability from green-by-design perspective. Moving from strategic synthesis planning to reaction science at step level, predictive modeling involving multivariate statistical analysis, machine learning, and newly developed computational quantum chemistry toolkits were introduced to help unravel the mechanistic insights, screen virtual libraries of catalyst designs, and predict reaction outcomes, demonstrating the effectiveness of these predictive approaches toward reaction optimization.
These evidently consonant efforts in applying computational methods/modeling and predictive analytics for organic synthesis have drawn increasing attention from practitioners in the pharmaceutical and fine chemical industries. This led us to establish this specialized symposium with interdisciplinary interests for the betterment of green chemistry. With the overarching theme of the conference reflecting ACS GCI’s mission to advance the implementation of green and sustainable chemistry and engineering practices across the global chemistry enterprise, this session will showcase the use of the state-of-the-art AI/machine learning approaches, rule-based expert systems, computational chemistry methods, and other predictive analytics approaches, to help guide synthesis design strategies and reaction optimization in making greener and more sustainable chemistries and processes.