Special Topic: AI for Chemistry
Open Access
Review

Figure 10

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Data-driven reactivity predictions. (A) An advanced model has been engineered for predicting pKa values, leveraging the ibond database which encompasses data from 39 distinct solvents [79]. Copyright©2020, John Wiley and Sons. The machine-learning-driven model employs SPOC to capture the molecular electronic and structural features. Training using either a neural network or the XGBoost algorithm has resulted in its remarkable predictive prowess, evidenced by a minimum mean absolute error (MAE) of 0.87 pKa units. (B) A broad-scale predictive model, developed using machine learning techniques, has been introduced [81]. Copyright©2023, John Wiley and Sons. This innovative molecular representation, designated as rSPOC, amalgamates structural, physicochemical, and solvent-related elements. The model’s dataset incorporates 1115 nucleophiles, 285 electrophiles, and 22 solvents, thereby establishing it as the most expansive resource for reactivity prediction currently in existence.

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