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Development of Artificial Intelligence-Assisted Organic Synthesis Platforms for Predictive Reaction Design, Catalyst Discovery, and Sustainable Molecular Manufacturing
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The rapid advances in AI, ML, and computational chemistry provide the framework to transition organic synthesis from trial-and-error experimentation to the predictive, data-driven exploration of new molecules, as the synthesis of complex pharmaceutical, agrochemical, natural products, polymers, and organic functional materials involves meticulous control over reaction pathways, selectivity, catalytic efficiency, and experiment parameters. Traditionally, the sequential empirical optimization of conditions and reagents leads to significant waste, requiring many experiment days and the consumption of reagents, chemicals, energy, and the experimental scientist's time; fortunately, burgeoning reaction databases, electronic lab notes, cheminformatic software and computational tools allow researchers to mine this data for the creation of predictive models that support successful and efficient organic synthesis (Schneider et al., 2015; Coley et al., 2019). AI approaches that analyze the chemical structure and its impact on chemical outcomes to accurately predict potential product class, possible product structure, yield, selectivity, and reaction conditions represent this innovation; beyond that, ML (such as neural networks, graphs, etc.) and DL systems now help retrosynthetic planning in the selection of viable disconnections that are experimentally feasible. Further supplementing these developments is the area of catalyst discovery, where machine-learning combined with computation takes a look at catalyst structure; the exploration of binding energies, activation barriers, transition states and reaction mechanism between the catalyst and substrate has already proven capable of systematically sifting through catalyst possibilities faster than traditional experimental searching methods (Butler et al., 2018) through the combination of molecular descriptors, DFT and quantum chemistry. Here the proposal of a holistic, close-loop framework wherein machine learning, deep learning, DFT and quantum chemistry are coupled with automated synthesizers and robotic experimenters is outlined as a new method for chemical discovery where prediction guides experiment, data improves models, and optimal conditions are measured in terms of speed, efficiency, selectiveness, cost, waste and safety; important considerations are the need for data, avoidance of bias, transparency of prediction and the verification of predictions. The integration of AI methods, with experimental chemistry remaining as the gold standard for verification, will therefore permit the accelerated synthesis and manufacture of the novel organic molecules we seek and it has great potential to both optimize conditions for efficient, sustainable organic synthesis and to contribute to greener manufacturing processes.
Keywords
Artificial intelligence, Machine learning, Organic synthesis, Reaction prediction, Computational chemistry
How to cite this paper
@article{1723040,
author = {Dr. K. S. Lamani},
title = {Development of Artificial Intelligence-Assisted Organic Synthesis Platforms for Predictive Reaction Design, Catalyst Discovery, and Sustainable Molecular Manufacturing},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {2711-2718},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723040.pdf},
abstract = {The rapid advances in AI, ML, and computational chemistry provide the framework to transition organic synthesis from trial-and-error experimentation to the predictive, data-driven exploration of new molecules, as the synthesis of complex pharmaceutical, agrochemical, natural products, polymers, and organic functional materials involves meticulous control over reaction pathways, selectivity, catalytic efficiency, and experiment parameters. Traditionally, the sequential empirical optimization of conditions and reagents leads to significant waste, requiring many experiment days and the consumption of reagents, chemicals, energy, and the experimental scientist's time; fortunately, burgeoning reaction databases, electronic lab notes, cheminformatic software and computational tools allow researchers to mine this data for the creation of predictive models that support successful and efficient organic synthesis (Schneider et al., 2015; Coley et al., 2019). AI approaches that analyze the chemical structure and its impact on chemical outcomes to accurately predict potential product class, possible product structure, yield, selectivity, and reaction conditions represent this innovation; beyond that, ML (such as neural networks, graphs, etc.) and DL systems now help retrosynthetic planning in the selection of viable disconnections that are experimentally feasible. Further supplementing these developments is the area of catalyst discovery, where machine-learning combined with computation takes a look at catalyst structure; the exploration of binding energies, activation barriers, transition states and reaction mechanism between the catalyst and substrate has already proven capable of systematically sifting through catalyst possibilities faster than traditional experimental searching methods (Butler et al., 2018) through the combination of molecular descriptors, DFT and quantum chemistry. Here the proposal of a holistic, close-loop framework wherein machine learning, deep learning, DFT and quantum chemistry are coupled with automated synthesizers and robotic experimenters is outlined as a new method for chemical discovery where prediction guides experiment, data improves models, and optimal conditions are measured in terms of speed, efficiency, selectiveness, cost, waste and safety; important considerations are the need for data, avoidance of bias, transparency of prediction and the verification of predictions. The integration of AI methods, with experimental chemistry remaining as the gold standard for verification, will therefore permit the accelerated synthesis and manufacture of the novel organic molecules we seek and it has great potential to both optimize conditions for efficient, sustainable organic synthesis and to contribute to greener manufacturing processes.},
keywords = {Artificial intelligence, Machine learning, Organic synthesis, Reaction prediction, Computational chemistry},
month = {May},
}