Exploring the Range of Knowledge-Based Prediction Applications in Chemistry
Rohini Narayan Shelke, Laxmi G. Kathawate, Dattatraya Navnath Pansare, Aniket P. Sarkate, Ajit Kalnyanrao Dhas, Pravin N. Chavan, Shailee V. Tiwari, Deepak K. Lokwani, Shivraj N. Mawale · Apple Academic Press eBooks · 2025
Predicting the outcomes of organic transformations is a vital and difficult undertaking in the field of molecular synthesis. The fusion of machine learning and chemical expertise presents a distinctive and potent approach for generating predictions in synthesis. This comprehensive analysis delves into 322 the most recent embedding techniques and model designs that facilitate the development of machine learning models capable of reliably predicting yield and selectivity in molecular synthesis. Recent advancements in the utilization of machine learning (ML) techniques in chemistry have showcased the potential for data-driven forecasting of synthesis efficiency. The integration of digitization and ML modeling plays a pivotal role in fully harnessing experimental data’s potential and accurately predicting performance and selectivity. Multiple studies have emphasized the importance of integrating chemical knowledge into ML models, enhancing their capacity to make predictions that surpass human capabilities. This succinct analysis provides an overview of state-of-the-art techniques and model designs in forecasting synthetic presentations, with a focus on the effective integration of chemical knowledge into machine learning as of June 2022. By incorporating strategies from organic synthesis and chemical information, our objective is to furnish chemists with a roadmap and inspiration for digitizing and automating organic chemistry principles. Chemists rely on their domain expertise to predict reaction efficiencies, considering factors such as reactant properties, molecular-level reaction mechanisms, optimal steps for rates and selectivity, and the quantum chemical basis of desired performance. This knowledge significantly enhances prediction accuracy, but remains a daunting task even for seasoned experts in synthesis and catalysis. Computational chemistry, which encompasses chemistry-based software, has yielded various applications, including chemical design, automated reaction synthesis, analysis of spectral data, and molecular docking.